{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from math import log, exp, sqrt, pi, exp\n",
    "from scipy.stats import norm\n",
    "import functools\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator, FixedLocator, FixedFormatter\n",
    "from matplotlib.dates import DateLocator, DayLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def call(T, F, X, sigma, A):\n",
    "    \"\"\"使用正态分布的Black模型进行估值的支付方互换期权，其中\n",
    "    T表示期权剩余期限，F表示标的合约利率，X表示执行利率，sigma表示年化波动率，\n",
    "    A表示贴现因子。\"\"\"\n",
    "    d = (F - X) / (sigma * sqrt(T))\n",
    "    price = A * sigma * sqrt(T) * (1/sqrt(2*pi)*exp(-d**2/2) + d*norm.cdf(d))\n",
    "    return price\n",
    "\n",
    "\n",
    "def put(T, F, X, sigma, A):\n",
    "    \"\"\"使用正态分布的Black模型进行估值的收取方互换期权其中\n",
    "    T表示期权剩余期限，F表示标的合约利率，X表示执行利率，sigma表示年化波动率，\n",
    "    A表示贴现因子。\"\"\"\n",
    "    d = (F - X) / (sigma * sqrt(T))\n",
    "    price = A * sigma * sqrt(T) * (1/sqrt(2*pi)*exp(-d**2/2) - d*norm.cdf(-d))\n",
    "    return price\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Spread(object):\n",
    "    \"\"\"构建一个价差策略，由不同的期权合约组成，用来计算价差对某个参数的敏感性\"\"\"\n",
    "    def __init__(self, types:list,  positions:list, par=10000000):\n",
    "        \"\"\"type参数表示够成价差的期权的类型，positions表示仓位，正值表示做多，负值表示做空\n",
    "        par表示一份合约的面值\"\"\"\n",
    "        self.types = types\n",
    "        self.positions = positions\n",
    "        self.par=10000000\n",
    "        self.fun = {\"call\": call, \"put\": put}       \n",
    "\n",
    "    def create_cal_value(self, factor_name, params):\n",
    "        \"\"\"本方法用于创建计算价差价值的函数，唯一的输入参数是敏感性相关的变量的值\"\"\"\n",
    "        def cal_value(factor):\n",
    "            res = 0\n",
    "            for t, p, param in zip(self.types, self.positions, params):\n",
    "                res = res + self.par*p * functools.partial(self.fun[t], **param)(**{factor_name:factor})\n",
    "            return res\n",
    "        return cal_value\n",
    "        \n",
    "    def get_values(self, factor_name, factor_range, others:list):\n",
    "        \"\"\"计算价差价值对某个因素的敏感性，实现方法是划定某个因素的范围与单位距离，\n",
    "        将该因素的每个假设值代入cal_value计算价差的理论价值，\n",
    "        参数factor是指选定的参数，facor_range是列表形式的选定参数的取值范围，\n",
    "        others则是字典形式其他因素的固定值\"\"\"\n",
    "        res = []\n",
    "        cal_value = self.create_cal_value(factor_name, others)        \n",
    "        res = map(cal_value, factor_range)\n",
    "        return list(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9879335473976283"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1/(1.0075**2)+1/1.0075**1.75+1/1.0075**1.5+1/1.0075**1.25)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9916313777201762"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1/(1.0075**1.5)+1/1.0075**1.25+1/1.0075**1.0+1/1.0075**0.75)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 跨式期权空头\n",
    "others = [{\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "factor = \"F\"\n",
    "spread1 = Spread([\"call\", \"put\"], [-1, -1])\n",
    "factor_range = [0.029, 0.03, 0.031]\n",
    "s1 = spread1.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 宽跨式期权空头\n",
    "others = [{\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "factor = \"F\"\n",
    "spread2 = Spread([\"call\", \"put\"], [-1, -1])\n",
    "factor_range = [0.029, 0.03, 0.031]\n",
    "s2 = spread2.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 看涨期权比例垂直价差\n",
    "others = [{\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.032, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "factor = \"F\"\n",
    "spread3 = Spread([\"call\", \"call\"], [1, -1.6783])\n",
    "factor_range = [0.029, 0.03, 0.031]\n",
    "s3 = spread3.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[62283.16766027322, 68937.66283153249, 75939.75791514247]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看涨期权比例垂直价差\n",
    "others = [{\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "factor = \"F\"\n",
    "spread3 = Spread([\"call\", \"call\"], [1, 0])\n",
    "factor_range = [0.029, 0.03, 0.031]\n",
    "spread3.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16663.745071284775, 19540.985461651075, 22765.825764368077]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看涨期权比例垂直价差\n",
    "others = [{\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "factor = \"F\"\n",
    "spread3 = Spread([\"call\", \"call\"], [0, 1])\n",
    "factor_range = [0.029, 0.03, 0.031]\n",
    "spread3.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.31280472550717"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(68937.66283153249-62283.16766027322)/(19540.985461651075-16663.745071284775)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 蝶式期权多头\n",
    "others = [{\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01},\n",
    "         {\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "factor = \"F\"\n",
    "factor_range = [0.029, 0.03, 0.031]\n",
    "spread4 = Spread([\"call\", \"call\", \"call\"], [1, -2, 1])\n",
    "s4 = spread4.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 看涨期权时间价差多头\n",
    "others = [{\"X\":0.029, \"A\":0.9916313777201762, \"T\":1, \"sigma\":0.01}, {\"X\":0.029, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "factor = \"F\"\n",
    "spread5 = Spread([\"call\", \"call\"], [-1, 1])\n",
    "factor_range = [0.029, 0.03, 0.031]\n",
    "s5 = spread5.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b, c, d = [s1[1]-s1[0], s2[1]-s2[0], s3[1]-s3[0], s4[1]-s4[0]]\n",
    "1, a/b, a/c, a/d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1.13291268511233, 2159.63438555374, 8.523736347322163)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GammaPlot(object):\n",
    "    \"\"\"绘制价差理论价值的Gamma风险的敏感度图\"\"\"\n",
    "    def __init__(self, factor_range, cost_factor):\n",
    "        self.factor_range = factor_range\n",
    "        self.k = [1, 1.13291268511233, 2159.63438555374, 8.523736347322163]\n",
    "        self.cost_factor = [cost_factor]\n",
    "        \n",
    "    def spread1(self):\n",
    "        \"\"\"跨式期权空头\"\"\"\n",
    "        others = [{\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "        factor = \"F\"\n",
    "        s1 = Spread([\"call\", \"put\"], [-1*self.k[0], -1*self.k[0]])\n",
    "        cost = s1.get_values(factor, self.cost_factor, others)[0]\n",
    "        res = [(r-cost) for r in s1.get_values(factor, self.factor_range, others)]\n",
    "        return res\n",
    "    \n",
    "    def spread2(self):\n",
    "        \"\"\"宽跨式期权空头\"\"\"\n",
    "        others = [{\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "        factor = \"F\"\n",
    "        s2 = Spread([\"call\", \"put\"], [-1*self.k[1], -1*self.k[1]])\n",
    "        cost = s2.get_values(factor, self.cost_factor, others)[0]\n",
    "        res = [(r-cost) for r in s2.get_values(factor, self.factor_range, others)]\n",
    "        return res\n",
    "    \n",
    "    def spread3(self):\n",
    "        \"\"\"看涨期权比例垂直价差\"\"\"\n",
    "        others = [{\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "        factor = \"F\"\n",
    "        s3 = Spread([\"call\", \"call\"], [1*self.k[2], -2.31*self.k[2]])\n",
    "        cost = s3.get_values(factor, self.cost_factor, others)[0]\n",
    "        res = [(r-cost) for r in s3.get_values(factor, self.factor_range, others)]\n",
    "        return res\n",
    "    \n",
    "    def spread4(self):\n",
    "        \"\"\"蝶式期权多头\"\"\"\n",
    "        others = [{\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}, {\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01},\n",
    "         {\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"sigma\":0.01}]\n",
    "        factor = \"F\"\n",
    "        s4 = Spread([\"call\", \"call\", \"call\"], [1*self.k[3], -2*self.k[3], 1*self.k[3]])\n",
    "        cost = s4.get_values(factor, self.cost_factor, others)[0]\n",
    "        res = [(r-cost) for r in s4.get_values(factor, self.factor_range, others)]\n",
    "        return res\n",
    "    \n",
    "    def plot(self):\n",
    "        \"\"\"画图\"\"\"\n",
    "        s1 = self.spread1()\n",
    "        s2 = self.spread2()\n",
    "        s3 = self.spread3()\n",
    "        s4 = self.spread4()\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
    "        ax.plot(self.factor_range, s1, label=\"跨式期权空头\")\n",
    "        ax.plot(self.factor_range, s2, label=\"宽跨式期权空头\")\n",
    "        ax.plot(self.factor_range, s3, label=\"支付方互换期权比例垂直价差\")\n",
    "        ax.plot(self.factor_range, s4, label=\"支付方互换期权蝶式价差多头\")\n",
    "        ax.set_xlabel(\"远期利率\")\n",
    "        ax.set_ylabel(\"价差理论边际\")\n",
    "        ax.legend()\n",
    "        fig.show()\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GUn5hgX48tF5fp69Q6pGdCgsKVYeY89UlNll1wmpXGOHkOKUd7Tr1ecs+YnTM6f6nspwjoxW9c2bMSQfr3XyXzu0hbh6VL+OdidK2nvyOSHlflfoUpzyXm/8ZO+5x1toK3x2r6P2lo+/22IrfLCx+Ljfe2XPrHR739mUrfpvtuGwV31fmz+N03pEseYg94atS91Pqa9GN76eUd6FO3mJkK97VsRdc+fuzx+2zgjGlbTj2T/zU5zGn/Ggq+Bmc9BoyZbxvXyf5krL34QU8Wa4PS4oovl1dpb8f5M4Yx7W65W6nIwDwE60k3SBpzd41+udP/9S/Uubq70dWqlNsJ93S+hZ1bdiVk9MAgA/xZHldqaJpHpLUTlLKGY4BAL/TNq6tnrrkKX1+7ecadsEw/XrgV909/25d/b+r9d9N/1VOQY7TEQEAbvDYaiHGmGhJiyXNl9RPRQdr/mStHVXOmE7W2ozy9stqIQD8UV5BnuamzNXMdTO1MX2jaobX1I0tb9T1La5XbHis0/EAIOB461J8sZL6SFpkrd11pmOOR7kG4M+stfpm1zeauW6mvt7+tcJd4brinCs0oPUAJcYkOh0PAAKGV5brqkC5BhAofjnwi2atn6UPf/lQ+YX56t6ou+5oc4eS6njvklQA4C8o1wDgp/Zl79O/fvqX/rXxX8rIyVDHeh01sO1AdarfiQ8/AkAVoVwDgJ/LysvSv3/+t2aum6k92XvUtnZbDWw7UD0a9VCQ8crFlgDAZ1GuASBA5Bbk6n+//E9vrHlDqYdT1axGM93R9g71Teyr4CBPrrgKAP6Lcg0AASa/MF+fpXym6Wuma/OBzYqvHq/b296uK8+5UqGuUKfjAYBPo1wDQIAqtIVauG2hpq2eprX716petXq6s+2durrZ1QpxhTgdDwB8EuUaAAKctVbLdizTKz++otV7V6tBtQa6q91d6n9Of4UEUbIB4HRQrgEAkopK9pIdS/TKqle0dv9axVeP1+B2g/WHpn9gTjYAuIlyDQA4gbVWi1IX6ZUfXtGGtA1qHN1Yg9sNVr/EfnIFuZyOBwBezd1yzVpNABAgjDHq3qi73r38XU3pOUXhrnA9tvgxXf3B1ZqbMleFttDpiADg8yjXABBgjDHqldBLs/vP1qQek+QyLj381cO66eObtHzncqfjAYBPo1wDQIAKMkHq07iP/t3/33qy65NKO5KmO+fdqbs+v0sb9m9wOh4A+CTKNQAEOFeQS1ecc4U+vPpDPZz8sNbtX6frPrpOjyx6RNsObXM6HgD4FD7QCAA4waHcQ3pz7Zv6+/q/K9/m67pzr9Og8wepVkQtp6MBgGNYLQQAcFb2ZO3R//34f/rvpv8qzBWm29rcptvOu00RwRFORwMAj2O1EADAWakTWUdjOo/RnCvnqEvDLnr1h1d1+ZzL9eEvH7KyCACUgXINAChXk5gmmtRjkt7q+5ZqR9TWiK9H6OaPb9YPe35wOhoAeB3KNQDALR3qdtA///BPPdn1Se3J2qMBnw7Qw189rB2HdzgdDQC8BuUaAOC2IBNUsrLI4HaD9eW2L9V/Tn+9+P2LyszLdDoeADiOcg0AOG2RIZG6J+kefXT1R+qT2EfT1kzT5XMu15xNc5iPDSCgUa4BAGesXrV6mnDJBL39+7fVoHoDjVk6RgM+GaD1+9c7HQ0AHEG5BgCctfPjztc/+v1DT3Z9UqmHU3XDRzdo/PLxysjJcDoaAHgU5RoAUCmMMSXzsW9qdZPe+/k99Z/Tn6kiAAIK5RoAUKmiQ6M1vONwzb58thJjEouminzKVBEAgYFyDQCoEi1qttDMvjOLpoocYqoIgMBAuQYAVJnSpopc8f4V+vCXD2WtdToeAFQ6yjUAoModP1UkPipeI74eocFfDNa2Q9ucjgYAlYpyDQDwmBY1W2hW31kacdEI/bj3R13zv2v05to3lV+Y73Q0AKgUlGsAgEe5gly6seWNev/K99W5QWdNWjlJN358o9buW+t0NAA4a5RrAIAj6lWrpxd7vajJPSYrLTtNN39ys5759hll5WU5HQ0AzhjlGgDgqN6Ne+v9q97Xn879k97e8Lau+t9VWpS6yOlYAHBGKNcAAMdFhUZpVKdRmtVvlqqFVNM98+/Ro4se1YEjB5yOBgCnhXINAPAaSXWSNPvy2bq73d2alzJPV/3vKi3YusDpWADgNso1AMCrhLhCNCRpiP51+b8UFxmn+768j6PYAHwG5RoA4JVa1Gyhd/7wDkexAfgUyjUAwGuFBHEUG4BvoVwDALweR7EB+ArKNQDAJ5R2FHvE4hE6lHvI6WgAUIJyDQDwKUePYg9uN1ifbPlE135wrVbuXul0LACQRLkGAPigkKAQ3ZN0j2b1m6XgoGD9Ze5fNHnlZOUV5DkdDUCAo1wDAHzW+XHn673+7+ma5tdoxtoZuvmTm/XLgV+cjgUggFGuAQA+LTIkUuMuHqcpPadoV+YuXf/R9Xp7w9uy1jodDUAAolwDAPxCr4Re+u+V/1XHeh014dsJGvLFEO3J2uN0LAABhnINAPAbtSNq65Xer2jURaO0cvdKXfPBNZr/23ynYwEIIJRrAIBfMcbo+pbXa3b/2WpYvaHuX3i/xi8fr5yCHKejAQgAlGsAgF9qEtNE/+j3D93a+la9u/Fd3fTxTfo141enYwHwc5RrAIDfCnGF6KELH9IrvV/R3qy9uuGjG/T+5vf5sCOAKkO5BgD4vW7x3fRe//fUpnYbjV4yWiO+HqHMvEynYwHwQ5RrAEBAqFutrqb1maa7k+7WJ1s+0fUfXa8N+zc4HQuAn6FcAwAChivIpSHthmj6pdOVnZ+tmz+5mTWxAVQqyjUAIOBcWO9C/bv/v3Vxg4s14dsJuu/L+3Qw96DTsQD4Aco1ACAgxYbH6qVeL+mRCx/R4tTFuv7D6/VT2k9OxwLg4yjXAICAZYzRgNYD9GbfN5VbmKs/f/Jnzdk0x+lYAHwY5RoAEPCS6iRp9uWzlRSXpDFLx2jc0nGcdAbAGaFcAwAgqVZELb3e53Xd2fZO/WfTfzTgkwHadmib07EA+BjKNQAAxVxBLg1rP0wv93pZqYdTdf1H1+urbV85HQuAD6FcAwBwku6Nuuvdy99VfPV43bvgXr34/YsqKCxwOhYAH0C5BgCgFI2iGunvv/+7/tj8j5q2Zpru+uIupR9JdzoWAC9HuQYAoAxhrjCNu3icHr/4ca3avUo3fnyjNqZtdDoWAC9GuQYAoAJXN79aM/vNVF5hngZ8OkCfpXzmdCQAXopyDQCAG9rUbqN3L39XLWJb6KGvHmIeNoBSeaxcG2NmGGOWGWNGlTMmxhjzqTFmnjFmjjEm1FP5AACoSO2I2ppx2YySedjDvhymQ7mHnI4FwIt4pFwbY66R5LLWdpbU1BjTvIyhN0uaZK29VNIuSX09kQ8AAHeFukI1tvNYjbpolJZuX6qbPr5JWzK2OB0LgJfw1JHrHpJmF9+eJ6lraYOsta9aaz8v/jJO0p7SxhljBhljVhhjVuzdu7eyswIAUC5jjK5veb2mXTpNB3MP6qaPb9Ki1EVOxwLgBaqkXBtjXjfGLDx6kTRU0vbiu9Mk1a3g8Z0lxVprl5d2v7V2qrU22VqbHBcXV5nRAQBwW3K9ZP3rD/9So6hGunf+vZq2epqstU7HAuCgKinX1tq7rLU9jl4kvSgpovju6uU9rzGmpqSXJN1eFdkAAKhM9avX18x+M9W3SV+9uOpFDV88XDkFOU7HAuAQT00LWaljU0HaSUopbVDxBxjfk/SYtfY3z0QDAODsRARH6JlLntF97e/TJ1s+0cDPBmp/9n6nYwFwgKfK9fuSBhhjJkm6TtLHxpjWxpjxJ427Q1J7SSOLp5Rc76F8AACcFWOMBrYdqOe7P6+f0n7SzZ/crM3pm52OBcDDjKfmhhljYiX1kbTIWrursvabnJxsV6xYUVm7AwDgrK3dt1ZDFwzVkfwjmth9oro07OJ0JABnyRiz0lqbXNE4j61zba1Nt9bOrsxiDQCAN2pTu43++Yd/qmH1hrp7/t3650//dDoSAA/hDI0AAFSBetXqaVa/WerWsJue+uYpPf3N08ovzHc6FoAqRrkGAKCKRIZEanLPybq19a1656d3NHTBUB3OPex0LABViHINAEAVcgW59NCFD2ls57FavmO5Bnw6QDsO73A6FoAqQrkGAMADrj33Wr3W5zXtztqtmz+5WRv2b3A6EoAqQLkGAMBDOtXvpFl9Zyk4KFi3zb1NS7cvdToSgEpGuQYAwIOaxTbT279/W42iGume+ffo/c3vOx0JQCWiXAMA4GF1Iuvorb5vKbleskYvGa3Xf3xdnjrvBICqRbkGAMAB1UOr69Xer+qKc67Qyz+8rL8t+xtL9QF+INjpAAAABKoQV4jGdxmvupF1NW3NNO3N3qvnuj2nyJBIp6MBOEMcuQYAwEHGGA1rP0yjO43W19u/1u2f3a592fucjgXgDFGuAQDwAte1uE5Tek7RLwd+0YBPBui3g785HQnAGaBcAwDgJXo06qE3LntDmXmZuuXTW7R+/3qnIwE4TZRrAAC8SNu4tprVb5bCXGG6/bPb9d2u75yOBOA0UK4BAPAyiTGJmtVvlupF1tPgzwdrwdYFTkcC4CbKNQAAXqhetXp6q+9balmzpR5Y+AAnmwF8BOUaAAAvVSO8hqZdOk0d63XU6CWjNXPdTKcjAagA5RoAAC8WGRKpl3u/rEsbX6qJKyZqyvdTOJsj4MU4iQwAAF4u1BWqZ7s9q5hvYjR9zXSlH0nX6E6j5QpyOR0NwEko1wAA+ABXkEujO41WjbAamrZmmg7mHtSESyYo1BXqdDQAx6FcAwDgI46ezTEmLEYTV0zU4dzDmtxzMqdLB7wIc64BAPAxt553q57o8oS+2fWNhnwxRJl5mU5HAlCMcg0AgA+6qtlVeuaSZ/Tj3h81aN4gZeRkOB0JgCjXAAD4rL5N+mpSj0nakLZBA+cNVNqRNKcjAQGPcg0AgA/rldBLL/V6SVsytuj2ubdrb9ZepyMBAY1yDQCAj+vSsIte+91r2pG5Q7fNvU07D+90OhIQsCjXAAD4gQvrXaipfaYq/Ui6bpt7m7Yd2uZ0JCAgUa4BAPATSXWSNP2y6crKz9Jtn96mXzN+dToSEHAo1wAA+JHWtVrrjcveUIEt0F/m/kU/p//sdCQgoFCuAQDwM81jm+vNvm8qOChYt392u35K+8npSEDAoFwDAOCHmsQ00Vt931JEcIQGzhuojWkbnY4EBATKNQAAfqpRVCO9cdkbigiO0B3z7qBgAx5AuQYAwI9RsAHPolwDAODnKNiA51CuAQAIAI2iGumNS99gDjZQxSjXAAAEiEbRRQU7PDicgg1UEco1AAAB5GjBDnOFUbCBKkC5BgAgwDSKbqQ3L3uTgg1UAco1AAAB6OSCvSl9k9ORAL9AuQYAIEAdLdihQaG6c96dSslIcToS4PMo1wAABLBG0Y007bJpsrIaOG+gUg+lOh0J8GmUawAAAlzTmKaa2meqsvOzNXDeQO3K3OV0JMBnUa4BAIBa1GyhqX2mKiMnQ3fOu1P7svc5HQnwSZRrAAAgSTqv9nl69XevanfWbt05704dOHLA6UiAz6FcAwCAEhfUuUAv9npRWw9u1aDPB+lg7kGnIwE+hXINAABO0Kl+J73Q8wVtOrBJd39xt7LyspyOBPgMyjUAADhFt/huerbbs1q7b62GLhiqI/lHnI4E+ATKNQAAKFWfxn00vut4fbfrO92/8H7lFuQ6HQnwepRrAABQpsubXq5xF4/Tku1L9Njix1RQWOB0JMCrBTsdAAAAeLdrml+jQ7mHNHHFREV/E60xncbIGON0LMArUa4BAECFbj3vVh3IOaDpa6arRlgN3df+PqcjAV6Jcg0AANwy7IJhysjJKCnYt553q9ORAK9DuQYAAG4xxmjkRSN1MPdg0RSR0Ghd3fxqp2MBXoVyDQAA3OYKcunprk/rUO4hjVs2TtFh0eqd0NvpWIDXYLUQAABwWkJcIXqhxwtqU7uNHv7qYX2781unIwFeg3INAABOW2RIpF7t/aoaRzfW0AVDtW7fOqcjAV7BrXJtjHEZY24+7uumxphHqi5lvgYHAAAgAElEQVQWAADwdjFhMXq9z+uKDY/VkC+G6NeMX52OBDiu3HJtjHnfGBMsqVDSQGNMuDFmmqQPJO31REAAAOC96kTW0dQ+U2WM0V2f36VdmbucjgQ4qqIj19WstfnWWls8NkjS55I6S3q3qsMBAADvlxCdoNf7vK7DuYc1+PPBysjJcDoS4JiKyrU97nZbSe9LGijpP5I+NsZ8WVXBAACA72hZs6Wm9JyirYe2atiCYcopyHE6EuCIisr18Uv1/WCtvVRFR6z7W2t7SkqtsmQAAMCndKzfUU91fUrf7/lejy1+TAWFBU5HAjyuonL9hjEmyBjjkhRsjAmVVF/SYmPMSEl3uftExpgZxphlxphRboyta4xZ5e6+AQCAd+jbpK8eTn5Yn//2uZ797lkVzSwFAkdFJ5EZIqmZpIWS/iLpZkkXSpouyUjKd+dJjDHXSHJZazsbY94wxjS31m4q5yETJUW4s28AAOBdbjnvFu3O2q1Z62epXrV6+kubvzgdCfCYisp1kKRlkvpJ6iJptqT7JL0nqbu1NtfN5+lR/FhJmiepq6RSy7UxppekTEllftzYGDNI0iBJSkhIcDMCAADwlAeTH9SerD2atHKS4iLjdHnTy52OBHiEKe/tGmPMTkmfqugo9VFWUoykDEmy1t5eyuNel9TiuE3dJSVZa380xlwqqb21dkIpjwuV9JmkqyW9b63tUdE3kJycbFesWFHRMAAA4GG5Bbka/MVgrdqzSq/2flWdG3R2OhJwxowxK621yRWNq+jI9aWS8nTiqiFHuYovp7DWnjAX2xgzRcemeVRX2XO9h0t61Vp7wBhTxhAAAOALQl2hmtxzsm6be5v+uvCveqvvW2pZs6XTsYAqVe4HGq21a6y1P1lrN5ZyWS+pg5vPs1JFU0EkqZ2klDLG/U7SPcaYhZKSjDHT3dw/AADwQtGh0Xqt92uKCo3SkC+GaPvh7U5HAqpUhac/N8b0MMasMcbMN8Z8Zox5zxjzdPGZG+8vvq7I+5IGGGMmSbpORWtktzbGjD9+kLW2m7W2R/F0kB+stQPP4HsCAABepG61unqt92vKKcjR4M8H68CRA05HAqpMheVaRVM/Jlpre0vqK+lOFa0gcqWkPGtthSuGWGsPquhDjcsl9bTWZlhr11try1yWz5351gAAwDc0i22ml3q9pB2Hd2jYl5xkBv6r3HJtjLlMUktJNSTJFjkg6VVJ6yS5u1qIrLXp1trZ1toyVwEBAAD+q0PdDnrykie1as8qjV4ymjWw4ZfKnNJhjIlU0aod9SU1MMbcoKIVQt6w1s4uHpPpkZQAAMAv9E3sq9RDqZry/RQlRCXo3gvudToSUKnKLNfW2ixJg4uXzmtirX3dGJMoaYwxZoikATqNI9cAAACSdEebO7Tt0Da9vvp1NYpqpCubXel0JKDSuPNhxJ8k7ZQka22KpNuNMXdI6izpw6qLBgAA/JExRqM6jdL2w9s1btk41a9WXx3rd3Q6FlApKppzHS3pWmvtGmPMYGPMWGPMGEnxku6QNNUTIQEAgH8JCQrRpB6TlBCVoPsX3q9fM351OhJQKSpaLWSipCxjjEtF00CWS/qm+HqzpKFVGw8AAPir6NBovdL7FYUEhejuL+5W2pE0pyMBZ62ik8gMstb+n7W2QNICa+1nRy8qKtapHkkJAAD8UnxUvF7q9ZL2Ze/TsAUs0Qff584610fNN8YkHPd1HTHnGgAAnKXz487X05c8rR/3/qhRX49SoS10OhJwxtwq18aYpiqaItLyuM1/kbSgKkIBAIDA0qdxHz3Q4QHNTZmrl1e97HQc4Iy5c/rzLpJeU9Fpy+81xnSXJGvtBEmmauMBAIBAcdt5t+nac6/VtDXTNGfTHKfjAGekvJPIVJP0tqQtkq601h4xxtwqabqkr4qHsc41AACoFMYYjbhohHYc3qHHlz2u+Kh4XVjvQqdjAaelvCPX+ZL+K6mVpD8bYy6WdJ+khsaYycaYJyQ18kBGAAAQIEKCQvRc9+cUHxWvBxY+oNRDrJ0A31JmubbW5lhrZ1lr+0qKkjRB0teSHpT0saS5knp6JCUAAAgY0aHRern3yyq0hRq6YKgO5x52OhLgNnfmXMdba1+Q9H+Soqy1SyRdL+lcay1/TgIAgErXOLqxnu/xvLZkbNHwxcNVUFjgdCTALRWdobGJpPeMMTGSmkpqW3yGxt2SJhtjLvNARgAAEIA61e+k4R2H66vUrzRl1RSn4wBuKfMDjcXulPRna22GMeZaScN0bIWQ7ySlV2U4AAAQ2G5oeYM2H9isN9e+qWY1mumKc65wOhJQrnLLtbV2xHFfvmmtXVTFeQAAAE7waMdHlZKRonFLxykhKkFJdZKcjgSUqaJpIa6jt621U4wxwcXbE4qvhxhjWOsaAABUmZCgED3f43nVq1ZP9315n3Ye3ul0JKBMFX2gcZcx5nNjzH5jzOeSdhRv/3vx9TXWWlt18QAAAKSYsBi93Otl5RbkatiXw5SVl+V0JKBUFZXrVdbaPpJWF1//WLw9p/iaYg0AADyiaY2merbbs/o5/WeN/HqkCm2h05GAU1RUrpONMQsktSu+bl983cEY86WkDlWeEAAAoNgl8ZfowQ4P6outX+iVH15xOg5wiorK9QprbS9JPxRfryy+/t5a21PS91WeEAAA4DgDWg/QNc2v0dTVUzUvZZ7TcYATVFSuE4rnWscVXycUbz86HYQPMwIAAI8yxmjkRSPVLq6dRi0ZpU3pm5yOBJSoqFz/VdI4a21bSY9K6lG8/V/GmNaSZrFaCAAA8LRQV6gm9ZikaiHVdN+X9ykjJ8PpSIAkN45cS7rBGHOLik4gc2fx7XMk3WGtncVqIQAAwAl1IuvohR4vaGfmTj266FFOkQ6vUFG5lqQjkjIl5UrKLr79vaSOxphqVZgNAACgXEl1kjTiohFasmOJXlr1ktNxgApPf54iaa21dokxZoek36y1OyTJGJMnqbaKyjYAAIAj/nTun7R+/3rNWDtDLWu1VN/Evk5HQgAr98i1tfYzSZuKby+T1OW4+z6QFFul6QAAANzwWMfHlBSXpDFLxmhj2kan4yCAuTMt5O/GmHrGmP6SBpkivYrvm1yF2QAAANxy9AOOUSFRfMARjiq3XBtjGqvobIzVJDXXsSX4Hiy+zqu6aAAAAO6Li4zTpJ6TtCdrjx5Z9AgfcIQjyizXxpg4Se9JaquiUl0gScWrg4QZY5pKCvNESAAAAHe0i2unkReN1NIdSzVl1RSn4yAAlVmurbV7rbUdVTTn+uQS3ULS33TspDIAAABe4Y/n/lHXnXud3lz7pj7d8qnTcRBgyl0txBhz9AOLM1RUsGOMMS5Ja6y1A4rP2ggAAOBVhnccrk0HNmnMkjFqGtNULWq2cDoSAkR500KiJc2VFCVpgKSPJR2S9IWKlugDAADwSiGukKIPOIZG6YGFD+hQ7iGnIyFAlDct5KCkzpL2Fm9KL749QFIbY0wnSVurPCEAAMAZqB1RWxO7T9T2w9s16utR4qTS8ISK1rkuVNHUkX2SvpQUZK1NlXStpOmS+KQAAADwWu3rttcDHR7Qgm0L9Oa6N52OgwBQ4TrX1trfW2szJLWUNNEYY6y1eyT9RUWnQwcAAPBaA1oPUJ/GfTTl+yn6btd3TseBn3PnJDIyxlwr6Upr7dzipfhkrf3OWrupStMBAACcJWOMnujyhBKiEvTQVw9pd+ZupyPBj1VYro0xPVR0lPpVY8wSY8y84svnxpjlVZ4QAADgLFULqabJPScrOz9bD331kPIKOQ8eqkZFZ2h8UdKdkq6y1i621nax1l5afOljre3kmZgAAABn55wa5+hvF/9NP+z9QZNWTHI6DvxURUeuF0uqJ+lyY0xNY8ytxpjLjDGtPZANAACgUvVr0k83t7pZ/9jwD83dMtfpOPBDFa0W8p6kyyT1ljRUUr6Kzsr4Z2PMYmPMxVUfEQAAoPI82OFBJcUlaczSMfrlwC9Ox4GfqWhaSDdrbb619l5JTSR9Ya2dZq0dIemPkgZ5IiQAAEBlCXGFaGL3iYoIjtBfF/5VmXmZTkeCHynvDI0uSTcaY5YbY96QVCjpaWPMG8VfTyjv8QAAAN6qbrW6erbbs/rt4G8au3QsJ5hBpQku6w5rbYGkIcaYCBVNCblJ0qOSVhQPCZIUVuUJAQAAqsBF9S/SsAuGafL3k3VBnQt0c6ubnY4EP1BmuT7KWpst6VljzBxJNay1+6s+FgAAQNW7vc3t+mHPD5q4YqLaxbVTm9ptnI4EH+f2tA5r7SZrLac1AgAAfsMYo/FdxysuIk4PffWQDuYedDoSfBxzpgEAQECLCYvRs92e1e7M3RqzZAzzr3FWKNcAACDgJdVJ0v0d7tf8rfP1zk/vOB0HPoxyDQAAIOmW1reoR3wPTVwxUWv3rXU6DnwU5RoAAEDMv0bloFwDAAAUiwmL0XPdn2P+Nc4Y5RoAAOA47eLalcy/fnvD207HgY+hXAMAAJzk6Pzr51c+rzV71zgdBz6Ecg0AAHCS4+dfP7zoYWXkZDgdCT6Ccg0AAFAK5l/jTFCuAQAAynB0/vWCbQuYfw23UK4BAADKcUvrW9Q9vrsmrZykDfs3OB0HXo5yDQAAUA5jjJ7o8oRiw2L1yKJHlJWX5XQkeDHKNQAAQAViw2P19CVP67eDv2nCtxOcjgMvRrkGAABwQ8f6HTWw7UDN2TxHn2751Ok48FIeK9fGmBnGmGXGmFFujH3VGNPfE7kAAADcNSRpiNrFtdPjyx5X6qFUp+PAC3mkXBtjrpHkstZ2ltTUGNO8nLGXSKpnrf3QE9kAAADcFRIUome6PSNJenTRo8orzHM4EbyNp45c95A0u/j2PEldSxtkjAmRNE1SijHmSs9EAwAAcF/D6g01tvNYrd63Wq/+8KrTceBlqqRcG2NeN8YsPHqRNFTS9uK70yTVLeOht0haL+lZSR2NMUPL2P8gY8wKY8yKvXv3VnJ6AACA8vVt0lfXNL9GM9bM0PKdy52OAy9SJeXaWnuXtbbH0YukFyVFFN9dvZznvUDSVGvtLkn/kNSzjP1PtdYmW2uT4+LiKjk9AABAxR698FE1jm6sEYtHKO1ImtNx4CU8NS1kpY5NBWknKaWMcZslNS2+nSzpt6qNBQAAcGYiQyL1XPfndCDngEYvGc3p0SHJc+X6fUkDjDGTJF0n6WNjTGtjzPiTxs2Q1NMYs0jS3ZImeigfAADAaWtZs6UeTH5Qi1IXcXp0SJKMp/7KMsbESuojaVHxtI9KkZycbFesWFFZuwMAADgt1loNXTBUS3cs1du/f1utarVyOhKqgDFmpbU2uaJxHlvn2lqbbq2dXZnFGgAAwGknnx49Oz/b6UhwEGdoBAAAOEux4bEa33W8Ug6m6PkVzzsdBw6iXAMAAFSCzg06a0DrAXp347talLrI6ThwCOUaAACgktzX/j41j22u0UtGa3/2fqfjwAGUawAAgEoS5grThEsm6HDuYY1dOpbl+QIQ5RoAAKASnRt7ru7vcL++Sv1K7/38ntNx4GGUawAAgEp2c6ub1al+Jz333XPakrHF6TjwIMo1AABAJQsyQXqy65MKCw7T8MXDlVeQ53QkeAjlGgAAoArUiayjcZ3Haf3+9Xrtx9ecjgMPoVwDAABUkd81/p2uanaVpq+ZrpW7VzodBx5AuQYAAKhCwzsOV8PqDTVi8Qgdyj3kdBxUMco1AABAFaoWUk1PX/K0dmft1lPfPOV0HFQxyjUAAEAVS6qTpEHnD9JHv36kT7d86nQcVCHKNQAAgAcMOn+Qzq99vp5Y/oR2Ze5yOg6qCOUaAADAA4KDgvXUJU8pvzBf45aO4+yNfopyDQAA4CGNoxvrrx3+qiU7lnD2Rj9FuQYAAPCg61tcr871O2viionaenCr03FQySjXAAAAHhRkgvR4l8cVbII1askoFRQWOB0JlYhyDQAA4GH1qtXTYxc9plV7Vmnm+plOx0ElolwDAAA44PKml6t3Qm+9vOpl/Zz+s9NxUEko1wAAAA4wxmhM5zGKCo3SyK9HKq8gz+lIqASUawAAAIfUDK+psZ3H6qe0n/Taj685HQeVgHINAADgoF4JvXTFOVdoxtoZWr13tdNxcJYo1wAAAA4b3nG46kTW0civRyo7P9vpODgLlGsAAACHRYVGaXyX8Uo5mKIp309xOg7OAuUaAADAC1xU/yLd1PImvb3hbX2z8xun4+AMUa4BAAC8xP0d7ldidKJGLRmlw7mHnY6DM0C5BgAA8BIRwRF6suuT2pO1RxNXTHQ6Ds4A5RoAAMCLnB93vm4971b9Z9N/tHT7Uqfj4DRRrgEAALzMPUn3qElME41bNo7pIT6Gcg0AAOBlwlxheqLLE9qdtVvPr3ze6Tg4DZRrAAAAL9Qurp1ubX2r/v3zv7VsxzKn48BNlGsAAAAvdXfS3UqMTtTYpWOVmZfpdBy4gXINAADgpcKDw0umh0xaMcnpOHAD5RoAAMCLJdVJ0i2tb9Hsn2dr+c7lTsdBBSjXAAAAXu6epHuKpocsYXqIt6NcAwAAeLmj00N2Zu7UCytfcDoOykG5BgAA8AFHp4e8u/FdfbPzG6fjoAyUawAAAB9x7wX3KjE6UWOWjGF6iJeiXAMAAPiI8OBwPd7lcaaHeDHKNQAAgA+5oM4FGtB6gN7d+K6+2/Wd03FwEso1AACAj7n3gnvVKKqRxi0dp+z8bKfj4DiUawAAAB8TERyhcZ3HaeuhrXrth9ecjoPjUK4BAAB8UMf6HfXH5n/UzPUztW7fOqfjoBjlGgAAwEc9kPyAaofX1pilY5RXmOd0HIhyDQAA4LOiQ6M1stNI/Zz+s95c+6bTcSDKNQAAgE/rldBLlyVepv/78f/0a8avTscJeJRrAAAAHze843BFhkRq3NJxKrSFTscJaJRrAAAAH1c7orYeufARrdqzSu9ufNfpOAGNcg0AAOAH+jftry4NumjyysnaeXin03ECFuUaAADADxhjNKbzGFlZPb78cVlrnY4UkCjXAAAAfqJB9Qa6r/19+nr71/ro14+cjhOQKNcAAAB+5IYWNygpLknPfves9mfvdzpOwKFcAwAA+BFXkEt/u/hvyszL1DPfPuN0nIBDuQYAAPAzTWs01V3n36VPUz7Vwm0LnY4TUCjXAAAAfuj2NrerWY1mevKbJ5WVl+V0nIBBuQYAAPBDIa4Qje08Vrszd+ulVS85HSdgUK4BAAD8VFKdJF3X4jq989M7Wrd/ndNxAgLlGgAAwI/d1/4+1Qqvpb8t/ZvyC/OdjuP3KNcAAAB+LCo0So9d9Jg2pG3Q2xvedjqO36NcAwAA+LnfJfxOPeJ76JUfXtH2w9udjuPXPFaujTEzjDHLjDGjyhkTa4z5xBizwhjzuqeyAQAA+DNjjEZcNEKS9OTyJzk1ehXySLk2xlwjyWWt7SypqTGmeRlDB0h621qbLCnKGJPsiXwAAAD+rn71+hp6wVAt3r5Yn/32mdNx/Janjlz3kDS7+PY8SV3LGLdfUhtjTA1JjSRtq/poAAAAgeGmljepda3WeubbZ3Qw96DTcfxSlZRrY8zrxpiFRy+Shko6OsEnTVLdMh76taTGkoZJ2lA8trT9DyqeOrJi7969lRseAADAT7mCXBrbeazSjqRp8srJTsfxS1VSrq21d1lrexy9SHpRUkTx3dXLed6xkgZbax+X9JOkv5Sx/6nW2mRrbXJcXFwlpwcAAPBfrWu11p9b/Vnv/fyevt/9vdNx/I6npoWs1LGpIO0kpZQxLlZSW2OMS9JFkphtDwAAUMnuSbpH9avV1+PLHldeQZ7TcfyKp8r1+5IGGGMmSbpO0sfGmNbGmPEnjXta0lRJGZJqSvqnh/IBAAAEjMiQSI3qNEq/ZPyiN9a+4XQcv+KRcm2tPaiiDzUul9TTWpthrV1vrR110rhvrbXnWWurW2v7WGsPeyIfAABAoOkW302XJV6mqaunKiUjxek4fsNj61xba9OttbOttbs89ZwAAAAo2/COwxXmCtP45eNZ+7qScIZGAACAAFU7oraGtR+mb3Z9o0+2fOJ0HL9AuQYAAAhgfzr3Tzqv1nl67rvnWPu6ElCuAQAAApgryKXRnUcrPSddL6962ek4Po9yDQAAEODOq3Werm9xvd7d+K7W7V/ndByfRrkGAACAhl4wVDXDa+qJZU+ooLDA6Tg+i3INAAAARYVG6eHkh7Vu/zq99/N7TsfxWZRrAAAASJL6Nemni+pfpBe/f1H7svc5HccnUa4BAAAgSTLGaNRFo3Sk4IgmrpjodByfRLkGAABAicSYRN3e5nZ9/OvH+mbnN07H8TmUawAAAJxgYNuBiq8er/HLxyu3INfpOD6Fcg0AAIAThAeHa2SnkUo5mKK31r3ldByfQrkGAADAKbo27Ko+jfto6uqp2nZom9NxfAblGgAAAKV69MJH5TIuPf3N07LWOh3HJ1CuAQAAUKq61erqnqR7tHj7Yi3YusDpOD6Bcg0AAIAy3dTqJp0be66e/vZpZeVlOR3H61GuAQAAUKbgoGCN6jRKu7N2a9qaaU7H8XqUawAAAJTrgjoX6IpzrtBb695SSkaK03G8GuUaAAAAFfprh78q3BWuCd9O4MON5aBcAwAAoEK1I2rrnqR7tGTHEj7cWA7KNQAAANxyQ8sb1Dy2uZ757hll52c7HccrUa4BAADgluCgYI3oOEI7M3dq+prpTsfxSpRrAAAAuC25XrJ+3+T3enPtm9p6cKvTcbwO5RoAAACn5cHkBxUSFMKHG0tBuQYAAMBpqRNZR3cn3a3F2xdr4baFTsfxKpRrAAAAnLabWt2kc2LO0TPfPaMj+UecjuM1gp0OUBXy8vKUmpqqI0f4RcM7hIeHKz4+XiEhIU5HAQCgUoQEhWjERSN0x7w79MbaN3R30t1OR/IKflmuU1NTFRUVpcTERBljnI6DAGet1f79+5WamqomTZo4HQcAgErTsX5H9U3sqxlrZqj/Of3VKKqR05Ec55fTQo4cOaJatWpRrOEVjDGqVasW76QAAPzSg8kPyhXk0rPfPut0FK/gl+VaEsUaXoXXIwDAX9WrVk+D2w3WwtSFWpS6yOk4jvPbcg0AAADPGNBqgJrENNGEbycotyDX6TiOolx7UHp6ern35+TkeCjJMQcOHNC+ffs8/rwAAMB/hLhCNLzjcG07tE2z1s9yOo6jKNdVoLCwUNOnT9f06dP19ttvKy8vT9Za9erVSwcPHlRhYaEKCgpOeUzdunX1yy+/aPTo0SfcN2rUKM2bN095eXlq3769JCkmJkY9evRQYmKiPvjgg5KxHTp0kCTl5uaqsLCwwqzvvPOOxo8fX+b9O3bsKLn91ltvaf78+RX/AAAAQMC5uMHF6tmop6aunqrdmbudjuMYynUVMMZoxowZatasmf7+979r0KBB6tChg/bv368WLVqoZ8+emj59+gnlNygoSDExMWratKkyMzO1dWvR6UTnz5+vmTNn6pFHHlGfPn20adMmTZs2TS1atNDChQt12223KTQ0VIWFhcrLy1P16tWVl5enp556Su3atdO9996re++9Vy1btpQk5efnq3nz5uratau6du2qWbNmaenSpSVft2jRouS5JWnq1Km65pprtG3bNm3cuFFBQbxkAABA6R5Oflj5hfma/P1kp6M4xi+X4jve3z5cp/U7DlbqPls3iNbY/ueVeb+1Vvn5+Tpw4IDy8/M1ffp0DR06VCNHjtRTTz2lIUOGaMWKFerRo8cJZTU9PV09e/aUJN1444368ssv1bt3b911113q+v/t3XtcVNX6+PHPmoFBFMUU0fISaUZW1vFWWCgjXsob0SlNUUNOgpej2UlL6utR7OQ9M43CCyIcFPEknRTKG8qU1zQttEAlf4CRFwRFTeU6+/fH4D6SolIgoc/79fL1mtnzzNprP+x2z+xZs5anJ2azGQ8PDwIDA1m2bFmZfe7bt49Jkyaxd+9eevfuzfz58/nhhx8IDQ0lKyuLzMxMwFb4Ozg48MUXX7B48WLGjx+PyWTi888/p2/fvjz//PN6m0eOHCEkJIRt27Zx+PBhCgsLqVevXmWmUgghhBB3kOb1mjP80eEsPbiUl91f5i+uf6nuLt12chuyCiilCAsLY968eUyfPp1evXqRlpaGn58fe/bsITg4mHPnzpGUlITFYsFisZCYmIi3tzfR0dFYLBZ27NiByWTS23z99dcxm83686NHj2I2m4mMjASgU6dOBAcH4+rqSlxcHG3btuXUKdtXMhaLBU9PTwCMRiNz5szB29sbg8GA0WikoKCA77//nieeeIJ+/fpx3333AbB48WK6du1KgwYN6NGjBxcvXqRu3bq3J4lCCCGEqJFGtB2Bq6Mrs/bMwqrdfIjqneaOv3N9ozvMVUUpxZdffomzs/N1x1BrmkbTpk3x9fXl6NGjWK1WHnvsMdq1a0dycjLNm187AXvPnj1xd3cnPDwcAFdXVxYtWkRoaKgeExERgZ2dHV5eXlgsFmrXrk1ubi7R0dEsXboUgHPnzrF48WKWL19O27ZtUUphZ2fH1KlTeeWVV/jwww85d+4c9evXZ+7cuezZs0efRi43NxcXFxfANrxE0zRZcVAIIYQQZdS2r80/Ov6Dt7e9zdqf1vJC6xequ0u31R1fXFeX77//HoCWLVtSXFxMSEgIffv25d577wVsxXF8fDyRkZHk5+czatQodu/eTXh4OP369dPb+eijj1i1ahUuLi6kpaWRnp5OaGgoTk5O5OTkcOnSJQDWrVuH0WikWbNmTJkyhfz8fF588UUCAgJwc3OjRYsWgO2HkP/6178ICgrCwcGBU6dOUVBQQIsWLSgoKGDJkiU0bNiQmJgYFixYoBfPmqaRnJyMj48PACUlJQQEBBAUFHTbciqEEEKImqHvA31ZfWg1H+7/kKozk+cAACAASURBVB7396Cu6e755luK6ypib29PYWEhTZo0oWnTpjg6OhIfH4/BYMDFxYVBgwaVic/OzqZ169akpKRw4sQJioqKuO+++xg3bhz29vZ07tyZCxcucObMGUpKSvD29sbT05PExEQA3NzcmDNnDkOGDMHb2xsAs9nMmDFjWLt2bZl9Pf744+zevRuwzQCSlZXF5MmTy8T4+fnh5+enP4+KiuLYsWP069ePdu3aVXq+hBBCCHHnUEoR/FQwgxMGsyh5EW92erO6u3TbSHFdBfbt28f+/ftp3bo1y5cv5+233yY+Ph6AjIwMJk6cSE5ODmvXriUuLo68vDySk5OZNGkSEydO5NVXX8Xd3R03Nzfatm3Lhg0bCAoKori4mBEjRnDw4EFiY2O5ePEiP//8M8888wyPP/44YBuuAZCQkMD777/Prl27GD16NCkpKQQGBlK/fv0KHYvVaiUsLIydO3eyZMkSAgMDMRgMLFy4kAYNGlRu4oQQQghxx3i04aO80PoFYlJjePGhF2np3LK6u3RbSHFdBRwdHdmwYQOtWrUqs33Xrl34+/vzwQcfYG9vT3p6OvPnz8fd3V2PcXNz4/Dhw4SFhZGUlISzszORkZEYDAZMJhPTpk1j48aNuLu7k5eXh6OjIx4eHvr78/Ly+Oyzz4iNjSUuLo6GDRtisViYOnUqO3bs4Ndff9WHeyilOHnyJAUFBfodcKvVSvv27Zk1axbh4eHExMQwaNAgVq5cCdjmxV63bh1eXl5s3ryZJk2a3IaMCiGEEKImeq3da2zK2MScvXMI6x6m/47rTqY0TavuPvwhHTt21L799tsy21JTU2nTpk019ejPrbDQtiTpleL6t0pKSigsLMTR0ZH169fTuXPn697tzsvLq/Bd8LudnJdCCCHuRv/+8d/M/XYuod6heDX3qu7u/G5KqX2apnW8WZxMxXeXMZlMmEymcj85Go1GHB0dAejdu3e5BbQU1kIIIYS4FYPbDOYB5weYvXc2hSWF1d2dKifFtRBCCCGEqDL2BnsmdZrEzxd+Jjolurq7U+WkuBZCCCGEEFXqmabPYG5mZsmBJZy+dLq6u1OlpLgWQgghhBBV7s1Ob1JkLeLD/R9Wd1eqlBTXt4HVatWnyLuZs2fP3jSmoKDgj3apwvLy8sjJybnt+xVCCCHEnaFFvRa88sgrrDu6jgOnD1R3d6qMFNe3wYwZM/j444/LbLNarYSHhxMeHs7KlSspKipC0zS8vb05f/48VquVkpKSa9qyWq00btz4usuqT548mU2bNlFUVET79u0B24qMZrMZNzc31q1bp8d26NABsM0eYrVab3oMMTExvPfee+W+fvz4cf1xZGQkW7ZsuWmbQgghhLi7BD4eiIujC3P2zqGmz1hXHpnnugrEx8czfvx4WrZsSW5uLnXq1KGgoIDIyEgaNmxISkoKGzduZNmyZcycOZNZs2aRmJhIcnIyubm5uLu789BDD/HKK6/g7++PwWDAYLB9DjIYDDg7O9OyZUsuXrzIsWPHaNGiBVu2bCEqKoqEhATq169PWloaS5cuxd3dHYvFQkhICCaTSS/anZycKCoqYsaMGcTFxeHlZZsaJzExkUOHDlFcXEybNm1o3LgxYFucxmq14unpCcDp06fZvHmzvqz6kiVLOHDgAAsWLODw4cPcf//91ZB5IYQQQvyZ1bGvw2vtXmPKzilsyNhA7wd6V3eXKt2dX1yvD4aTByu3zSZtofescl+2t7fH39+ftm3bsmrVKp588klycnI4c+YMvXv35siRI9SpU4fi4mLy8vIoLi4mPDyccePG8X//93/MmDGD0aNH89hjjxEZGUlERIReXINt6Ei3bt0AGDx4MElJSXTv3p2RI0fi6emJ2WzGw8ODwMBAli1bVqZv+/btY9KkSezdu5fevXszf/58fvjhB0JDQ8nKyiIzMxOwLVvq4ODAF198weLFixk/fjwmk4nPP/+cvn378vzzz+ttHjlyhJCQELZt28bhw4cpLCykXr16lZlxIYQQQtwhfFr5sOrQKj7Y9wHdmnejll2t6u5SpZJhIVXgueee45133tEL1Fq1alGrVi0cHBwwGAwEBwfzwAMPEBYWxrx585g+fTq9evUiLS0NPz8/9uzZQ3BwMAsWLGD48OEkJSVhsViwWCwkJibi7e1NdHQ0FouFHTt2YDKZ9H2//vrrmM1m/fnRo0cxm81ERkYC0KlTJ4KDg3F1dSUuLo62bdty6tQpACwWi35n2mg0MmfOHLy9vTEYDBiNRgoKCvj+++954okn6NevH/fddx8AixcvpmvXrjRo0IAePXpw8eJF6tate3uSLYQQQogaxWgw8manNzl58SRRP0ZVd3cq3Z1/5/oGd5irkr29Pa+99hrNmzfn2LFjaJqGpmls374dX19flFJ8+eWXODs7X3f8tKZpNG3aFABfX1+OHj2K1Wrlscceo127diQnJ9O8efNr9tuzZ0/c3d0JDw8HwNXVlUWLFhEaGqrHREREYGdnh5eXFxaLhdq1a5Obm0t0dDRLly4F4Ny5cyxevJjly5fTtm1blFLY2dkxdepUXnnlFT788EPOnTtH/fr1mTt3Lnv27NEXpsnNzcXFxQWwDSfRNA17e/vKT7IQQgghaqROTTrRo0UPlv2wjBdav4Brbdfq7lKlufOL62rk7u5OYmIiPj4+REVFcc8995S5q/z9998D0LJlS4qLiwkJCaFv377ce++9gK0wBtsY7sjISPLz8xk1ahS7d+8mPDycfv366W199NFHrFq1ChcXF9LS0khPTyc0NBQnJydycnK4dOkSAOvWrcNoNNKsWTOmTJlCfn4+L774IgEBAbi5ueljqJ2dnfnXv/5FUFAQDg4OnDp1ioKCAlq0aEFBQQFLliyhYcOGxMTEsGDBAr141jSN5ORkfHx8ANty6gEBAQQFBVVtsoUQQghRo7zR4Q2+yvqKhfsX8p5n+ZMm1DRSXFehunXrcvbsWbKzs5k+fTqBgYE8+OCD+uv29vYUFhbSpEkTmjZtiqOjI/Hx8RgMBlxcXBg0aNA1bWZnZ9O6dWtSUlI4ceIERUVF3HfffYwbNw57e3s6d+7MhQsXOHPmDCUlJXh7e+Pp6UliYiIAbm5uzJkzhyFDhuDt7Q2A2WxmzJgxrF27tsy+Hn/8cXbv3g3YZgDJyspi8uTJZWL8/Pzw8/PTn0dFRXHs2DH69etHu3btKieRQgghhLjjNK/XnKFthrL8x+UMbjOYRxs+Wt1dqhRSXFehTz75hGHDhjFu3Dg2b96MpmmEh4ezfft2vvnmG/bv30/r1q1Zvnw5b7/9NvHx8QBkZGQwceJEAHJycli7di1xcXHk5eWRnJzMpEmTmDhxIq+++iru7u64ubnRtm1bNmzYQFBQEMXFxYwYMYKDBw8SGxvLxYsX+fnnn3nmmWd4/PHHAfR5txMSEnj//ffZtWsXo0ePJiUlhcDAQOrXr1+hY7VarYSFhbFz506WLFlCYGAgBoOBhQsX0qBBg0rMqhBCCCHuFIGPB7L26Frm7JlD5HOR+hDTmkyK6yqQkpLCoEGDsFqtvPXWWwwZMoRz584xaNAgnJ2dycvL49///je+vr60atWqzHt37dqFv78/H3zwAWC7u52ens78+fNxd3fX49zc3Dh8+DBhYWEkJSXh7OxMZGQkBoMBk8nEtGnT2LhxI+7u7uTl5eHo6IiHh4f+/ry8PD777DNiY2OJi4ujYcOGWCwWpk6dyo4dO/j111/14R5KKU6ePElBQYF+B9xqtdK+fXtmzZpFeHg4MTExDBo0iJUrVwK2ebHXrVuHl5cXmzdvpkmTJlWddiGEEELUMHVNdRnbbizv7nqXzZmb6eXWq7q79Iepmj6Bd8eOHbVvv/22zLbU1FTatGlTTT2yjTv+6aefaN26dbX14Y8qLCwE0Ivr3yopKaGwsBBHR0fWr19P586dr3u3Oy8vr8J3we9U1X1eCiGEEH9GJdYSBiQM4FLRJdb6rsXB6FDdXboupdQ+TdM63ixOpuKrAkqpGl1YA5hMJkwmU7lfzxiNRhwdHQHo3bt3uQW0FNZCCCGEuBGjwchbnd7il19/YUXKiuruzh8mxbUQQgghhKhWHvd6YG5uZunBpeRczqnu7vwhUlwLIYQQQohqN6HDBAqKCwj9LvTmwX9iUlxXs+zsbM6dO3fL8TV9jPytWrt2LUVFRddsT05OJjs7+3e3W1BQgNVqLff1K7OowM1zffXf7Y/0SQghhBDg5uzG4DaD+SztMw6dOVTd3fndbltxrZRqrJTadpMYe6VUvFJqh1Lqb7erb1VtxYoV+oqJvxUWFkZsbGyZbVfPCvJbvXr14uTJk9d97cqPEMt77UqxmJaWxrhx4wDIz89nwIAB1y0kJ0+ezKZNmygqKqJ9+/aAbXEZs9mMm5sb69at02M7dOig76e84vXKNIBXPPLII9eNO336NNOnT2fhwoV4e3szbNgwtm7dCkBsbCzp6emAbVaW8PBwFi9ezKxZs3jjjTcYMmQIzz77rL6ke1FREYmJifry8QEBAbz11ltllpO/2tixY7FYLACsWbNGz9NvWa1WevbsSW5uLgB9+/bV+yWEEEKI32fk4yNxdnBm7t65NfaG4m2Zik8pdQ8QBdS5Seg4YJ+maSFKqS+VUp9qmnah6ntY+Z5++mkcHR1RSpGdnU1JSYleRF++fJk1a9bg6urK+vXr9WLuinvuuUd/XFJSgqZp2NnZ/lRBQUGkpqbqU9tZrVY0TcNoNPLSSy9x/vx5fvzxR1q3bo3JZNLbKSoqIj4+ngYNGmBvb4+Dg+2XuGFhYWRmZuLl5UVqaiopKSk0atSILVu2EBUVRUJCAvXr1yctLY2lS5fi7u6OxWIhJCQEk8mE1WqlpKQEJycnioqKmDFjBnFxcXh5eQGQmJjIoUO2T59XfgB5hZOTk36MERERjBgxAqUUS5YsYciQIYwfP54vvviC6Oho0tPTGTx4MO7u7hiNRsC2guX9999PvXr1SEpKwt3dXZ/C8Aqr1UpWVha1atUC4OTJk7Rr107/gJKfn6/HXrp0if379/PJJ59QVFREWFgYjRs3JikpiW7duulxvXv3xmg0opTSF/rJzs5m3LhxFBQUEB0dLVMPCiGEEL+Ds4MzY/4yhhnfzCDp5yS8W3hXd5cq7HbNc10CvAysvUmcGQguffw10BFIqrpuVZ2dO3fqj9esWcOvv/7K8OHDy8TExMQAEB4ezqeffqrPzJGSkoLZbGbs2LE4OTnx7rvvYjAYOHDgwDV3f61WK9OmTaNnz576neQuXbpgsVj0IvRqkZGRfPHFF6SmpnL58mVOnDjBN998w/LlyzEYDDRq1AiA7t27M3LkSDw9PTGbzXh4eBAYGMiyZcvKtLdv3z4mTZrE3r176d27N/Pnz+eHH34gNDSUrKwsMjMz9djfzjxiMBhITU1l7NixNG3alMGDB1NQUMC8efP44IMPOHbsGEeOHMHf35+AgAD9A8EVf/vb3zh9+jS1atXizJkzWK1W/vvf/3L69GkcHR3ZunUrDg4ODB8+nEGDBpGTk8OPP/5IUVERDg4OODk58fnnn+vthYaG8sgjj2AwGBg3bhxjxoyhf//+vPDCC5w+fZqBAwfqf7ecnByUUgwdOpRly5bh4OCApmnUqVNHCmshhBDiDxjw0ABWHVrF/H3z6dKsC/YG++ruUoVUSXGtlFoMXD22Yaumae/ewqo7dYBfSh+fARqX034QEATQokWLGzY4e8/sSh+383CDh5n05KQbxmRnZ+Pr64vJZOL06dOUlJQQGRnJs88+y9tvv01eXh4LFy6kVq1ajB49mhEjRuh3Vz08PLBYLJSUlGA0GnnuuecA293w7du333C/Z8+eJS0tje7du+vPu3btykcffQTA8OHDMZvNhIaGMnDgQAYMGEC3bt04ePAgbdu2JSYmhk2bNuntvf7662Wm0zt69Chms5mMjAw8PDzo1KkTwcHBBAUFERcXh7Ozsz4kw2Kx4Onpqb/38uXLeHp6cvnyZQICAjhx4gQTJ05k5syZPPnkk4CtwO3Tpw9g+9Axb948Dh48eN2hJuvWrSMkJASz2UxWVhbFxcX68f3224CMjAxmzZpVZtukSf/7Gx45coSVK1fSrl07/P39cXNzw2w24+DgQHBwMB9//DHFxcX4+fmRkZFBUlISZ8+epbCwkM2bNwO2O/CtW7fGx8fnhn8jIYQQQpTPzmDHhA4TGLt1LP85/B+GtBlS3V2qkCoprjVNG/k73/or4AicA5xKn1+v/SXAErAtIvM791WlXF1dSUpKwsfHh+TkZOzs7Jg4caJebG7bto3p06czc+ZMjEYjU6dOpWPHjvj6+upt/PbOc0ZGRpliNSsri3Xr1pW5m71jxw5GjhzJtGnTAIiIiODy5ctl2klLS2P79u3079+fTp06sXr1ap5//nkSEhL0YQ5X9OzZE3d3d33MuKurK4sWLSI09H+/5I2IiMDOzg4vLy8sFgu1a9cmNzeX6Oholi5dqsc5OjqW+XCwYsUKvvjiizL7mzRpEnFxcRw7doytW7dSUFDA7t27CQgIKBMXFRXFihUrSE9P58svv9R/qBgbG8uBAwcwm828+OKL+pjpkpKSa8aqX12wnz17llmzZrF69WpmzZrFli1biI+PJyAggODgYHbu3ImmaWzYsIG5c+eilCInJ4fz58+TkJCgt/Pll19y+fJlXn75ZYQQQgjx+3Rt1pWnmjzFouRF9G/Vn3qmetXdpVv2Z1v+fB/gCawBngB2/9EGb3aHuSo5ODgwbNgw3nvvPfr06UNOTg5dunQBoH///gDMnDkTgGHDhjFhwoQyxfXVjh8/Tvv27csUcv369cPV1bVM3LZt24iPj+err74CIDMzs8yPKaOjo0lOTsbT05PPP/+c7777jv79+7Nv3z58fX05cOAA8+bNw2QysWrVKlxcXEhLSyM9PZ3Q0FCcnJzIycnh0qVLgO3usdFopFmzZkyZMoX8/HxefPFFAgICcHNzu+k3C7915duNBg0aYLFY+Prrr0lMTKRWrVplimF/f3/8/f1v+c71hQsXWLRoUZltZ86c0R8/9dRT5OTksHr1au69914uXbpE8+bNAahdu7bet549e9KrVy9++eUXhg4dyt69e7G3t31ddWX8+y18QyOEEEKIG1BKMbHTRAbGDyT8QDhvdHyjurt0y6qtuFZKeQOPaJp29WSGUcCXSqkuwCPAN9XSuUo0dOhQevXqRUREBPv27Ss3zt3dvcz4399avHjxNYV3bm7uNcX17NmzmT17tv68T58+PPzww/rzYcOG0aVLF0JDQ5k/fz7z589n6tSp5OXlMWrUKH0ICtiWPu/cuTMXLlzgzJkzlJSU4O3tjaenpz7LhpubG3PmzGHIkCF4e9t+dGA2mxkzZgxr1147xL64uJioqCh+/bXslxInT57E1dUVg+F/E9jY2dmxfft2Bg0aRKNGjZg1axZLlizRXx84cCBHjx4lMTGRvLw8NE1jxYoV5OTk0LVrVxITE/UfddapU4eXXnqpzD4jIiLKzXd6ejpPP/30NduNRiPZ2dn4+fnx6KOP4uPjw8GDB3nooYdwcHBgwoQJ9OjRo9x2hRBCCHFrHm7wMD6tfFiRuoKB7gNpVrdZdXfpltzW4lrTNPNVj7cCW3/zeqZSqie2u9dTNE0ruZ39qywFBQUkJSWxf/9+kpOTGTRoEC4uLgwfPpymTZvSqlUrXn75Zdzc3Mrcjb0yI8jVcy0D7Nq1i/Xr17Njxw592/nz58nPz9eL0eLiYn0Gi6vl5OTQtGnTMrOOFBcXo5SisLCQOXPmkJWVxZYtW/Dx8eHChQsMGDCArVu3smHDBoKCgiguLmbEiBEcPHiQ2NhYLl68yM8//8wzzzyjD0m50ueEhATef/99du3axejRo0lJSSEwMBAnJyeOHTtGp06d6NOnDxMmTCAiIoLCwkLs7OwYMWIEY8aMoU+fPmiahqZpbN68Wb/TbjKZyM/PJzMzUx8us2rVKsBW8K5YsUK/c61pGiUlJWWO1WQy8Ze//KVMbq7cDbdardjZ2el/i1OnTvH1118zffr0a/62e/bsISAggKVLl+rF99ChQwkJCeHBBx+syGkihBBCiJsY124cGzM2smD/AuZ6za3u7tySP9uwEDRNOw78p7r78Uc4ODhw/PhxunXrRnBwsF4A+/j48NNPP7F79279jvPVC6VcuHCBJ598kn79+unbMjMzeeutt1i7dq0+/GDMmDHs3LmTsWPH6nERERFERUVdU1ybTCY8PT3RNI033niDHj16EBgYyDvvvMOoUaPo0qULkydPBiAuLo5x48ZRr149OnToQGRkJAaDAZPJxLRp09i4cSPu7u7k5eXh6OiIh4eHvp+8vDw+++wzYmNjiYuLo2HDhlgsFqZOncqOHTvo27cvEyZMYNiwYfqx+/r60r17dzRNo2XLljz77LN6TvLz81m2bBnx8fH6sIzly5eTmppKq1atANsPJufNm1fmbveaNWvQNI2//vWvvPrqq8TExBAZGUmdOnUICQkpk5vatWvTs2dPBg4cyMiRIykoKKC4uJg333yT2bNn6+2eP39ef4+zszMrV65k4cKFTJkyBbD9GHL48OHUqlWLwsJCPvzwQ31ecCGEEEL8fo3rNMb/UX8WH1jM0EeG8kSjJ6q7SzelauoE3Vd07NhR+/bbb8tsS01NpU2bNtXUo8pXXFys39UWNdeddl4KIYQQt8Olokv0/W9fmjo1Jbp3dLX9tkkptU/TtI43i5Plz2sAKayFEEIIcbeqbV+bsX8ZS/LpZDZlbrr5G6qZFNdCCCGEEOJPzfdBX1rf05oP931IYUlhdXfnhqS4FkIIIYQQf2pGg5GJHSaS9WsWqw6tqu7u3JCMNxBCCCGEEH96Tzd9mjc7vslzDzx38+BqJHeuq1l2djbnzp275fia/gPU36OgoKDc166etvBmubk6z9nZ2TeMvRvzLIQQQvzZvfLoK7jWdr15YDWS4vo2WLFiRZlVEq8WFhZGbGxsmW3u7u7lttWrV69rlvG+orCw/DFIhYWFesGYlpamLwuen5/PgAEDrltMTp48mU2bNlFUVKRPLefs7IzZbMbNzY1169bpsR06dND3c/Xc3Vc7fvw4f//73wH46quvWLx4MX5+flitVkaMGHHd91itVho3bszRo0f55z//ec3rY8eO1VdjXLNmjX5c12unZ8+e5ObmAtC3b1/S09OvG7ty5Uo+/vjj67529bFcERkZyZYtW24YL4QQQoi7gwwLqSJPP/00jo6OKKXIzs6mpKREL6IvX77MmjVrcHV1Zf369dcs1X3PPffoj69e/AUgKCiI1NRUmjRpAvxvyW2j0chLL73E+fPn+fHHH2ndurW+OiHY5o6Oj4+nQYMG2Nvb4+DgANiK+8zMTLy8vEhNTSUlJYVGjRqxZcsWoqKiSEhIoH79+qSlpbF06VLc3d2xWCyEhIRgMpmwWq2UlJTg5OREUVERM2bMIC4uDi8vLwASExM5dOgQZ86cYc+ePZw+fZpvv/2W1157jfz8fM6dO4e3tzepqanMnTuXf/zjHxgMBn2OaYPBgLOzMy1btuTixYscO3ZMX1L90qVL7N+/n08++YSioiLCwsJo3LgxSUlJdOvWTT/23r176wvsDBo0CLDduR43bhwFBQVER0fr+QwNDSU8PJz69euzZs0a8vLyaNKkCQkJCWVmbVmyZAkHDhxgwYIFHD58mPvvv/8PnjFCCCGEuBPc8cX1yRkzKEg9VKltOrR5mCbvvHPDmJ07d+qP16xZw6+//srw4cPLxMTExAAQHh7Op59+qs/bmJKSgtlsZuzYsTg5OfHuu+9iMBg4cOCAviLiFVarlWnTptGzZ0/9TnKXLl2wWCz6SoZXi4yM5IsvviA1NZXLly9z4sQJvvnmG5YvX47BYKBRo0YAdO/enZEjR+Lp6YnZbMbDw4PAwECWLVtWpr19+/YxadIk9u7dS+/evZk/fz4//PADoaGhZGVlkZmZCcB3331HVFQUly5d4p133uHjjz/m0KFDbNu2jaioKPz9/XnzzTeJjIwkIiKizMIwZ8+e1YvlwYMHk5SUhMlkIjQ0lEceeQSDwcC4ceMYM2YM/fv354UXXuD06dMMHDhQz3NOTg5KKYYOHcqyZctwcHBA0zTq1KmjF9bBwcG0aNGCMWPG0KBBAx566CEiIyOZNm1amcL6yJEjhISEsG3bNg4fPkxhYSH16tW74fkghBBCiLvDHV9cV5fs7Gx8fX0xmUycPn2akpISIiMjefbZZ3n77bfJy8tj4cKF1KpVi9GjRzNixAhq1aoFgIeHBxaLhZKSEoxGI889Zxu4//TTT7N9+/Yb7vfs2bOkpaXRvXt3/XnXrl356KOPABg+fDhms5nQ0FAGDhzIgAED6NatGwcPHqRt27bExMSwadP/5pB8/fXXqV+/vv786NGjmM1mMjIy8PDwoFOnTgQHBxMUFERcXBzOzs6cOnUKsK2g6OnpCdiWKO/QoQN5eXlkZGTo7dWtWxez2Yy3t7fev2HDhukfDIqLi3nppZf46KOPaN68uf6+I0eOsHLlStq1a4e/vz9ubm6YzWYcHBwIDg7m448/pri4GD8/PzIyMkhKSuLs2bMUFhayefNmwPatQOvWrfHx8QFg1qxZXLhwAYvFwjvvvIO7uzuvvvoqdevWLZPjxYsXs3fvXsLCwnj00UdZs2bNNTFCCCGEuDvd8cX1ze4wVxVXV1eSkpLw8fEhOTkZOzs7Jk6cqBeb27ZtY/r06cycOROj0cjUqVPp2LEjvr6+ehu/vfOckZGhvx8gKyuLdevWlbmbvWPHDkaOHMm0adMA27Loly9fLtNOWloa27dvp3///nTq1InVq1fz/PPPk5CQoA+buKJn638BdQAAD6FJREFUz564u7vrY8ZdXV1ZtGgRoaGhekxERAR2dnZ4eXlhsVioXbs2ubm5REdHs3TpUsD2A8GjR49y8eJF8vPzqVevHgcPHuTixYs8+eSTWK1WTpw4wb333ouvry9Hjx7FarXy2GOP0a5dO5KTk8sU12fPnmXWrFmsXr2aWbNmsWXLFuLj4wkICCA4OJidO3eiaRobNmxg7ty5KKXIycnh/PnzJCQk6O18+eWXXL58mYEDB/LKK69gMpno378/06dPZ9iwYWRmZjJ79myMRiOffvopzs7OzJ07lz179ujfNOTm5uLi4gLYPgxomqYvVS+EEEKIu8sdX1xXJwcHB4YNG8Z7771Hnz59yMnJoUuXLgD0798fgJkzZwIwbNgwJkyYUKa4vtrx48dp3759mcKwX79+uLqW/cXstm3biI+P56uvvgIgMzOzzI8po6OjSU5OxtPTk88//5zvvvuO/v37s2/fPnx9fTlw4ADz5s3DZDKxatUqXFxcSEtLIz09ndDQUJycnMjJyeHSpUsArFu3DqPRSLNmzZgyZQr5+fm8+OKLBAQE4Obmpo+Prlu3Lm3btiUlJYWnnnqKjRs3cvDgQTIyMmjQoAH169enTZs2vPzyy8THxxMZGUl+fj6jRo1i9+7dhIeH069fP/04nnrqKXJycli9ejX33nsvly5d0ovv2rVrA6CUomfPnvTq1YtffvmFoUOHsnfvXr3wvTJeXSmFUoro6GgyMjKYMWMGubm5fP311zzxxBPs3LmTiIgIGjRoQExMDAsWLNDb0DSN5ORk/e53SUkJAQEBBAUFVexkEUIIIcQdQYrrKjZ06FB69epFREQE+/btKzfO3d2dzz//vNzXFy9efE3hnZube01xPXv2bGbPnq0/79OnDw8//LD+fNiwYXTp0oXQ0FDmz5/P/PnzmTp1Knl5eYwaNUofggJgb29P586duXDhAmfOnKGkpARvb288PT1JTEwEwM3NjTlz5jBkyBB9aIfZbGbMmDGsXbtWb6tjx464ubkxYcIEYmNj2bRpE0ajkc8++wyr1UpsbGyZ4SdXZGdn07p1a1JSUjhx4gRFRUXcd9991ywJn56eztNPP33N+41GI9nZ2fj5+fHoo4/i4+PDwYMHeeihh3BwcGDChAn06NEDsBXG8+bNw9/fn+bNmzNs2DDeeOMNwsPDWblyJQB+fn74+fnp7UdFRXHs2DH69etHu3btrvenE0IIIcRdRIrrKlBQUEBSUhL79+8nOTmZQYMG4eLiwvDhw2natCmtWrXi5Zdfxs3Nrcy0dVcKxqvnbgbYtWsX69evZ8eOHfq28+fPk5+fr//wr7i4WJ8R42o5OTk0bdq0zKwjxcXFKKUoLCxkzpw5ZGVlsWXLFnx8fLhw4QIDBgxg69atbNiwgaCgIIqLixkxYgQHDx4kNjaWixcv8vPPP/PMM8/oQ1Ku9DkhIYH333+fXbt2MXr0aFJSUggMDGT58uXExsbStWtXateuzaJFi/jmm2/o27cvLVu2pG/fvnzwwQe4u7uzdu1a4uLiyMvLIzk5mUmTJjFx4kReffVV3N3dcXNzY/z48XruTp06xddff8306dOv+Vvs2bOHgIAAli5dqhffQ4cOJSQkhAcffLBMrNFo5KOPPuL8+fNs2LABJycnEhISWLly5TU/WLRarYSFhbFz506WLFlCYGAgBoOBhQsX0qBBg1s8U4QQQghxp5Hiugo4ODhw/PhxunXrRnBwsF4A+/j48NNPP7F79279jnNRUZH+vgsXLvDkk0+WGf6QmZnJW2+9xdq1a/WhCGPGjGHnzp2MHTtWj4uIiCAqKuqa4tpkMuHp6Ymmabzxxhv06NGDwMBA3nnnHUaNGkWXLl2YPHkyAHFxcYwbN4569erRoUMHIiMjMRgMmEwmpk2bxsaNG3F3dycvLw9HR0c8PDz0/eTl5fHZZ58RGxtLXFwcDRs2xGKxMHXqVHbs2MHo0aP5xz/+ocePHz+eJUuW8Mknn9CqVSs+/fRT8vLysLe3Jz09nfnz55eZ79vNzY3Dhw8TFhZGUlISYPsQU1xczJtvvsns2bP1PJ8/f15/n7OzMytXrmThwoVMmTIFsP0Ycvjw4dSqVYvCwkI+/PBD2rdvz4gRI0hNTeWee+6he/fuREZG6rOnXJGfn094eDgxMTEMGjRIv6MdExPDunXr8PLyYvPmzfoMJEIIIYS4u6iavhJdx44dtW+//bbMttTUVNq0aVNNPap8xcXF1wyDEJXvyvjrm1m/fj2dO3e+7jCWvLy8626HO++8FEIIIe4mSql9mqZ1vFncHVux3WqhVBNIYX173Or50rt373JfK6+wrukfYoUQQghxa+7I5c9r1apFbm6uFDTiT0HTNHJzc/V5zIUQQghx57ojb4k2a9aMrKwsTp8+Xd1dEQKwfeBr1qxZdXdDCCGEEFXsjiyu7e3teeCBB6q7G0IIIYQQ4i5zRw4LEUIIIYQQojpIcS2EEEIIIUQlkeJaCCGEEEKISlLj57lWSp0GMqth1y5ATjXst6aSfFWc5KxiJF8VJzmrGMlXxUnOKkbyVXG3M2f3a5rW6GZBNb64ri5KqW9vZSJxYSP5qjjJWcVIvipOclYxkq+Kk5xVjOSr4v6MOZNhIUIIIYQQQlQSKa6FEEIIIYSoJFJc/35LqrsDNYzkq+IkZxUj+ao4yVnFSL4qTnJWMZKvivvT5UzGXAshhBBCCFFJ5M61EEIIIYQQlUSKayGEEEIIISqJFNdCCHGXUko1UEr1VEq5VHdfagrJWcVIvipOclbz3fXFtVJqmVJql1Jq8q3GKKWclVLrlVKblFL/VUqZymvrVtqvaSorZ+Vss1NKHVNKWUr/tb19R1Y1KjFf182NUmqaUmqvUurj23VMVa0Sczb6qnx9r5RaLOeYnq97gATgSSBJKdWovLbkOlZ+zuQ6Vn5MOfmS69gNYsrJmVzHbhKjlGqslPruRnG38zp2VxfXSqm/AkZN0zoDLZVSrW8xZgjwgaZpvYCTwHPXi7uV9muaysxZOdseB1ZpmmYu/Xfw9hxZ1ajkfF2TG6VUB8AT24U4WynV4zYdWpWpzJxpmhZ2JV/ANmApco5diXkceEPTtOnARqC9XMduGnNNzpDr2I1irpcvuY7dOOaanMl17JZi3gccy4u73dexu7q4BszAf0ofb8L2H/dNYzRN+0TTtM2l2xoB2eW0dSvt1zRmKiln5eTRA+inlNpT+inTrgqO4XYyU3nn2PVy4wXEabZpfzYCXarkKG4vM5WXMwCUUk2BxpqmfYucY1fy9ZWmabuVUl2xFTW7ymnrVtqvacxUUs7kOlZ+TDnnmFzHbhBTTs4AuY6VF6OU8gYuYvtwW17crbRfae724roO8Evp4zNA44rEKKU6A/domra7nLhbab+mqcycXW/bXqCHpmlPAvZAn0o/gturMvN1vdzIOXYL5xjwdyCs9LGcY6UxSikFvAycBYrKiZNz7MY5o3S7XMduLV9yHbtJTHnnGHIduyZG2Ybl/hMIvklbt/Ucu9uL618p/RoBcOL6+bhujFKqAfAR8LcbxN1K+zVNZebsetsOaJp2ovTxt0BN/wq6MvN1vdzIOXbzc8wAdAMspZvkHCuN0Wz+DhwAfMqJk3PsxjmT69gNYq6TL7mO3SSmnHNMrmPXjwkGPtE0Le8mcbf1HLsTTuA/Yh//+2rgCSDjVmJKPyl9CrytaVrmDdq6lfZrmkrLWTl5jFZKPaGUMgK+QHKVHMXtU5nn2PVyI+fYjXMGtq+Yvyn9yhnkHLuSr0lKqVdKt9UH8sppS86xG+RMrmPlx5Rzjsl17AYx5eQM5DpWXkwP4O9KKQvwF6VUeDlxt/cc0zTtrv0H1MN2Qn4ApJYm/L2bxDgDo7F9XWMp/fdyOXHXbKvuY/6T5ex62x7D9mn9IDC9uo/3T5ava3KD7QPyDmABcBh4oLqP+c+Us9LYGcBfr3qvnGO2fN0DbAa+Bj4BlFzHflfO5DpWsXzJdayCOSuNlevYTa5FgOUGeb2t17G7fvlzZZv2pifwtaZpJ39vTHlxt/remqQyc3Y3qOp8KaUcgb7Afk3T/t8f7e+fgZxjFSPXsYqTc6xi5DpWcXKOVcyddB2764trIYQQQgghKsvdPuZaCCGEEEKISiPFtRBC1BClX2tWRbuqdPovIYQQf5AU10IIUQOUTsX1n9IZAtorpbYppbYqpbYo2/LHAaVx7ymleiml7JVS+0u3nSuNyVBKXZnaq5VSylcpFYZt/lefq/a178qiFEop+6u2j1dKDbrqeU1fuEIIISqdXBiFEKIG0DTNqpTyB/w1TZvJdVaxU0p1B/yBftim8GqtlAoEDmuaZlZKhQCFpeH/B+QCT2FbYvlk6Z3xi0CRpmnFpXE7lVKXASvQAjimlBqFbeaH2kqprpqmXa6iwxZCiBpHimshhKghNE07DsxUSvXCtnLb1b9INwG7gcXAdk3TLEqp3ZqmLVVKvXqdtv6mlKoFdCotrBUwGwj/TegGYA/gArQC/h/wM9AUaCWFtRBClCXFtRBC1ABKqReAfwBppf8ewVZQu2CbG/gY/1tI4kOl1NUrlrUqXWTBDditlHoYmAXUBh5VSsVhm6s5H9sd6qvNKH3fP7EtIfwTtrmc+wAhlXiIQghxR5Ax10IIUQNomvZfTdO6Ao01TZsFbALisN2tXqVp2mxN006Vhm8GVlz19mxgFJBQ2tYhTdN8ga3Y/j8wRdO0EeXs+mFsQ00mAd8B7wNLsS3F7F+JhyiEEHcEKa6FEKJmegjb3WoAlFJOSqlxwGDgSWzjrh9QSo0FfsV2h7v2VfH3Y1u0IwP4WCn10G93oJRyBkYCc4HXgPuAiYAjtiLbtXT5byGEEKVkWIgQQtQwSqmGgC/wL2wFcm1sBfAybGOxdwF1gQaAEdiqadp2pVSP0vc7Y7uzPRHbOOtAbD9urAXYX7WrF4EHStsFaFTa7i/Ylqq2B3K4dpy2EELctaS4FkKIGkIp5QG0AWYC4zVNK1JKxZc+zwScgeeAJdiu7+FAW2CQUqoO0BzYAbQEPqB0jLamaWmluxhRuh9Vuj0CiLhq/68DJzVNi63aIxVCiJpLimshhKgBlFIvA57YpuB7FXhLKTUF248aDUA74BAwXNM0K1ColJoKPKtp2mGlVH3gMrBb07QLwHelBbfdb/azDNtsINfj8Nt4IYQQZSlN024eJYQQoloppZRWzgW7dKEXA7b5qX8724cQQojbSIprIYQQQgghKonMFiKEEEIIIUQlkeJaCCGEEEKISiLFtRBCCCGEEJVEimshhBBCCCEqiRTXQgghhBBCVJL/D7sBnecwvmg8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor_range = list(np.arange(0.02, 0.04, 0.0001))\n",
    "gamma_plot = GammaPlot(factor_range, 0.029)\n",
    "gamma_plot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "class YangZhangVol(object):\n",
    "    \"\"\"本类用于计算数据序列的Yang-Zhang波动率序列\"\"\"\n",
    "    def __init__(self, data):\n",
    "        \"\"\"接受二维的样本数据data，一行为一个交易周期，列则分别为交易周期内的开盘价、收盘价、最高价与最低价\"\"\"\n",
    "        self.data = data\n",
    "        self.o, self.c, self.h, self.l = list(zip(*data))\n",
    "        self.num = len(data)\n",
    "\n",
    "    def sigma0_square(self, N):\n",
    "        \"\"\"N则表示以滚动N个交易周期估算波动率，最后形成一个波动率的时间序列\"\"\"\n",
    "        res = []\n",
    "        for i in range(self.num+1-N):\n",
    "            d = self.o[i:i+N]\n",
    "            dd = [(d[k+1]-d[k]) for k in range(N-1)]\n",
    "            res.append(np.var(dd, ddof=1))\n",
    "        return res\n",
    "\n",
    "    def sigma1_square(self, N):\n",
    "        res = []\n",
    "        for i in range(self.num + 1 - N):\n",
    "            d = self.c[i:i + N]\n",
    "            dd = [(d[k + 1] - d[k]) for k in range(N - 1)]\n",
    "            res.append(np.var(d, ddof=1))\n",
    "        return res\n",
    "\n",
    "    def sigma2_square(self, N):\n",
    "        res = []\n",
    "        for i in range(self.num + 1 - N):\n",
    "            d = self.data[i:i+N]\n",
    "            dd = [((d[k][2]-d[k][1])*(d[k][2]-d[k][0]) + (d[k][3]-d[k][1])*(d[k][3]-d[k][0])) for k in range(N)]\n",
    "            res.append(np.mean(d))\n",
    "        return res\n",
    "\n",
    "    def get_vol(self, N, year=252):\n",
    "        \"\"\"year表示周期波动率年化，默认为将日波动率年化\"\"\"\n",
    "        sigma0s = self.sigma0_square(N)\n",
    "        sigma1s = self.sigma1_square(N)\n",
    "        sigma2s = self.sigma2_square(N)\n",
    "        k = 0.34/(1.34+(N+1)/(N-1))\n",
    "        res = []\n",
    "        for a, b, c in zip(sigma0s, sigma1s, sigma2s):\n",
    "            res.append(np.sqrt(year*(a + k*b + (1-k)*c)))\n",
    "        return res      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VegaPlot(object):\n",
    "    \"\"\"绘制价差理论价值的Vega风险的敏感度图\"\"\"\n",
    "    def __init__(self, factor_range, cost_factor):\n",
    "        self.factor_range = factor_range\n",
    "        self.k = [1, 1.120322737483777, 1.8834061518739, 9.31098112386955]\n",
    "        self.cost_factor = [cost_factor]\n",
    "        \n",
    "    def spread1(self):\n",
    "        \"\"\"跨式期权空头\"\"\"\n",
    "        others = [{\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}, {\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}]\n",
    "        factor = \"sigma\"\n",
    "        s1 = Spread([\"call\", \"put\"], [-1*self.k[0], -1*self.k[0]])\n",
    "        cost = s1.get_values(factor, self.cost_factor, others)[0]\n",
    "        res = [(r-cost) for r in s1.get_values(factor, self.factor_range, others)]\n",
    "        return res\n",
    "    \n",
    "    def spread2(self):\n",
    "        \"\"\"宽跨式期权多头\"\"\"\n",
    "        others = [{\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}, {\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}]\n",
    "        factor = \"sigma\"\n",
    "        s2 = Spread([\"call\", \"put\"], [-1*self.k[1], -1*self.k[1]])\n",
    "        cost = s2.get_values(factor, self.cost_factor, others)[0]\n",
    "        res = [(r-cost) for r in s2.get_values(factor, self.factor_range, others)]\n",
    "        return res\n",
    "    \n",
    "    def spread3(self):\n",
    "        \"\"\"看涨期权反套利价差\"\"\"\n",
    "        others = [{\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}, {\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}]\n",
    "        factor = \"sigma\"\n",
    "        s3 = Spread([\"call\", \"call\"], [-2.31*self.k[2], 1*self.k[2]])\n",
    "        cost = s3.get_values(factor, self.cost_factor, others)[0]\n",
    "        res = [(r-cost) for r in s3.get_values(factor, self.factor_range, others)]\n",
    "        return res\n",
    "    \n",
    "    def spread4(self):\n",
    "        \"\"\"蝶式期权空头\"\"\"\n",
    "        others = [{\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}, {\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03},\n",
    "         {\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}]\n",
    "        factor = \"sigma\"\n",
    "        s4 = Spread([\"call\", \"call\", \"call\"], [1*self.k[3], -2*self.k[3], 1*self.k[3]])\n",
    "        cost = s4.get_values(factor, self.cost_factor, others)[0]\n",
    "        res = [(r-cost) for r in s4.get_values(factor, self.factor_range, others)]\n",
    "        return res\n",
    "    \n",
    "    def plot(self):\n",
    "        \"\"\"画图\"\"\"\n",
    "        s1 = self.spread1()\n",
    "        s2 = self.spread2()\n",
    "        s3 = self.spread3()\n",
    "        s4 = self.spread4()\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
    "        ax.plot(10000*self.factor_range, s1, linestyle=\"-\", label=\"跨式期权空头\")\n",
    "        ax.plot(10000*self.factor_range, s2, linestyle=\"--\", label=\"宽跨式期权多头\")\n",
    "        ax.plot(10000*self.factor_range, s3, linestyle=\"-.\", label=\"支付方互换期权比例垂直价差\")\n",
    "        ax.plot(10000*self.factor_range, s4, linestyle=\":\", label=\"支付方互换期权蝶式价差多头\")\n",
    "        ax.set_xlabel(\"波动率（bp)\")\n",
    "        ax.set_ylabel(\"价差理论边际\")\n",
    "        ax.legend()\n",
    "        fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 跨式期权空头\n",
    "others = [{\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}, {\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}]\n",
    "factor = \"sigma\"\n",
    "spread1 = Spread([\"call\", \"put\"], [-1, -1])\n",
    "factor_range = [0.011, 0.01]\n",
    "s1 = spread1.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 宽跨式期权空头\n",
    "others = [{\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}, {\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}]\n",
    "factor = \"sigma\"\n",
    "spread1 = Spread([\"call\", \"put\"], [-1, -1])\n",
    "factor_range = [0.011, 0.01]\n",
    "s2 = spread1.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 看涨期权比例垂直价差\n",
    "others = [{\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}, {\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}]\n",
    "factor = \"sigma\"\n",
    "spread1 = Spread([\"call\", \"call\"], [1, -2.31])\n",
    "factor_range = [0.011, 0.01]\n",
    "s3 = spread1.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 蝶式期权多头\n",
    "others = [{\"X\":0.025, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}, {\"X\":0.03, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03},\n",
    "         {\"X\":0.035, \"A\":0.9879335473976283, \"T\":1, \"F\":0.03}]\n",
    "factor = \"sigma\"\n",
    "factor_range = [0.011, 0.01]\n",
    "spread4 = Spread([\"put\", \"put\", \"put\"], [1, -2, 1])\n",
    "s4 = spread4.get_values(factor, factor_range, others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1.120322737483777, 1.7104163931049998, 9.31098112386955)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, b, c, d = [s1[1]-s1[0], s2[1]-s2[0], s3[1]-s3[0], s4[1]-s4[0]]\n",
    "1, a/b, a/c, a/d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-511920.36090775294, -5726.830188025313]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program files\\python\\python36\\lib\\site-packages\\matplotlib\\figure.py:445: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  % get_backend())\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor_range = np.arange(0.007, 0.013, 0.0001)\n",
    "vega_plot = VegaPlot(factor_range, 0.011)\n",
    "vega_plot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unindent does not match any outer indentation level (<tokenize>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<tokenize>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    res = []\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unindent does not match any outer indentation level\n"
     ]
    }
   ],
   "source": [
    "def get_vol(data, N):\n",
    "        \"\"\"计算收盘价-收盘价绝对波动率\"\"\"\n",
    "    res = []\n",
    "    num = len(data)\n",
    "    for i in range(num-N):\n",
    "        d = data[i : i+N+1]\n",
    "        dd = [(d[k + 1] - d[k]) for k in range(N)]\n",
    "        res.append(np.std(dd, ddof=1))\n",
    "    res = 100*np.array(res)\n",
    "    return res "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CC_Sigma_Plot(object):\n",
    "    \"\"\"绘制收盘价-收盘价波动率图，包含两个维度，一个是5天（一星期），一个是20天（一个月）\"\"\"\n",
    "\n",
    "    def __init__(self, data1, data2, dts, n1=5, n2=20):\n",
    "        \"\"\"根据读取csv文件, 其中data1是1YFR007互换利率，data2是5YFR007互换利率数据，dts则是日期序列\"\"\"\n",
    "        self.data1 = data1\n",
    "        self.data2 = data2\n",
    "        self.dts = dts\n",
    "        self.n1 = n1\n",
    "        self.n2 = n2\n",
    "\n",
    "    @staticmethod\n",
    "    def get_vol(data, N):\n",
    "        \"\"\"计算收盘价-收盘价绝对波动率\"\"\"\n",
    "        res = []\n",
    "        num = len(data)\n",
    "        for i in range(num - N):\n",
    "            d = data[i: i + N + 1]\n",
    "            dd = [(d[k + 1] - d[k]) for k in range(N)]\n",
    "            res.append(np.std(dd, ddof=1))\n",
    "        res = 100 * np.array(res)\n",
    "        return res\n",
    "\n",
    "    def plot(self):\n",
    "        fig, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=\"all\")\n",
    "        minor_locator = FixedLocator([21, 79])  # 划分时间段，分割点分别为2.15日和4.17日\n",
    "        # 互换利率曲线作图\n",
    "        d1 = self.data1[self.n2:]\n",
    "        d2 = self.data2[self.n2:]\n",
    "        axes[0].plot(self.dts, d1, linestyle=\"-\", label=\"1Y_FR007互换\")\n",
    "        axes[0].plot(self.dts, d2, linestyle=\"--\", label=\"5Y_FR007互换\")\n",
    "        axes[0].xaxis.set_minor_locator(minor_locator)\n",
    "        axes[0].grid(axis=\"x\", which=\"minor\", linestyle=\"--\")\n",
    "        axes[0].legend()\n",
    "        axes[0].set_title(\"互换利率走势图(%)\")        \n",
    "        # 1Y_FR007互换波动率作图\n",
    "        d3 = self.get_vol(self.data1[self.n2 - self.n1:].values, self.n1)\n",
    "        d4 = self.get_vol(self.data1.values, self.n2)\n",
    "        axes[1].plot(self.dts, d3, linestyle=\"--\", label=\"5D波动率\")\n",
    "        axes[1].plot(self.dts, d4, linestyle=\"-\", label=\"20D波动率\")\n",
    "        axes[1].legend()\n",
    "        axes[1].set_title(\"1Y_FR007互换5D与20D波动率(bp)\")\n",
    "        axes[1].xaxis.set_minor_locator(minor_locator)\n",
    "        axes[1].grid(axis=\"x\", which=\"minor\")\n",
    "        # 5Y_FR007互换波动率作图\n",
    "        d5 = self.get_vol(self.data2[self.n2 - self.n1:].values, self.n1)\n",
    "        d6 = self.get_vol(self.data2.values, self.n2)\n",
    "        axes[2].plot(self.dts, d5, linestyle=\"--\", label=\"5D波动率\")\n",
    "        axes[2].plot(self.dts, d6, linestyle=\"-\", label=\"20D波动率\")\n",
    "        axes[2].legend()\n",
    "        axes[2].set_title(\"5Y_FR007互换5D与20D波动率(bp)\")\n",
    "        axes[2].xaxis.set_minor_locator(minor_locator)\n",
    "        axes[2].grid(axis=\"x\", which=\"minor\")\n",
    "        # 展示\n",
    "        fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_csv(\"IRS_FR007_1Y.csv\", header=0, index_col=0, engine=\"python\", parse_dates=True)[\"2018-12-4\":]\n",
    "data2 = pd.read_csv(\"IRS_FR007_5Y.csv\", header=0, index_col=0, engine=\"python\", parse_dates=True)[\"2018-12-4\":]\n",
    "dts = data1.index[20:]\n",
    "sigma_plot = CC_Sigma_Plot(data1, data2, dts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E53dBgRJpH3vnOvzUEW5fgiErwLdW+q+7+StY8x68dBAKlcn08IVwpCNHjlC9enVHDyPLnThxgn379tG1a9d7ZdNsbUtu1KhRHDhwINXqEXFxccyePZszZ87w6aef8uWXXzJu3DjeeOMNmjZtCsCxY8d44YUX+PXXXylRIun7kq3XXykVqLUOSOs5SVAshBAiqWUvw+6foFQ96PB/UO6x9B0XfQd+fRIu7YOBv0OF5mkfk9i1E/B9A+j4KTw6OuPjFiIHyA1B8eXLl+nfv3+SbdWqVbtXQi03e5CgWHKKhRBCJNDa5PU2HA5PfJGQ6xt+FTy8wcX27A8AK16F4F3Q95eMB8QARStD8UfgyFIJioXIQr6+vqxfv97Rw8hxJKdYCCFEAqWg+w/Q6cuEgPjCHhhfDY7/df9jW7wCPSbDI09m/vrVu8G5bSYIv58b52HHFIiJyvy1hBAikXQFxUopH6VUO6VU0awekBBCCAcJvwKXgsztxPl+vrUhvw8cWGD7uONrzAyzT0Wo0+/BxlCrD3T8zFSgiLgGUTfNzHXyVL/zO2Dlq7Bv5oNdTwghrNIMipVShYFlQCNgnVKqmI19CiqlViql/lJKLVJKST0dIYTIbbZ+B1Naw61LSbc7u0CNHvDPnxCVrGXt9kkwsxcE2amQftHK0HiEWdz3v2bwaVn4qBh8UAT+zw/+/tjsV7MXuHvDwd/tc10hRJ6Xnpni2sBYrfXHwCqgvo19ngYmaK3bA5cBqakjhBC5SWSYWVxXowd4l0z5eK0+EBsFR5cnbDuwAP58Hap3hdp97T+mx9+C9h+b381egvqDwNXDPKYUNPk3nN0Cty7a/9pCiCwRHR1938fiC0AcP36cMWPGABAVFUWfPn3I6uIQaS6001pvAFBKtcDMFn9gY5+Jie4WA67Ya4BCCCGywc6pEB0OzV62/bhfQyhUFg4uMLfXvGsC5HJNoeeP4ORs+7gHUW/g/R+v2RPW/x8cWgRNnrf/9YXIhWJjY6lYsSIVK1YEoE2bNhQtWpTRo0ezZMkS/vzzTyZOnJjiuJ9//pn333+fcuXKAdCsWTNcXFyYO3cuRYoUIV++fCxYsICCBQsybNgwDh8+TOfOnXnrrbcAUmybNGkSc+fOBeDGjRs0btyYyZMn07t3b27dusWhQ4eoUqVKkoYdMTExLF26FB8fH1xdXe+VdZs0aRJnz56lZcuWHDlyhMOHD1OsWIrEhQeWruoTyhST6weEASmrLifs1wQorLXebuOxEcAIgLJly2ZqsEIIIbJA4M+w4TOo1hl8a9reRyno/j8TGGsLnNtuAuhmLyfM3ma3olXAvwu45nfM9YXIgfbv389TTz3FZ599BsCtW7do27YtI0eO5Ntvv03SSjm5YcOG3QtyAd577z3efPNNBg4cyPvvv8+sWbMoUaIEcXFxbNu2jaFDh3L8+HEOHDiQYtvo0aMZPdpUkRkzZgzPPPMMAEuWLAGgefPmrF+/Pkl76Hg///wzy5cv58iRI0RGRnLp0iV27NjB9OnTcXJyypKAGNIZFGszX/28UupDoBswN/k+Sikf4DugVyrnmAJMAVOnOLMDFkIIYWdeJaBKB+iecvYoifJNE26/chScXbN2XOnRXxbaiRxueueU22p0h0bPmdreM/ukfLzuAKj3NESEwrzBSR8bsjzl/ols376dZcuWsW7dOmrVqsXkyZNp374977zzDn5+fpQvXz5TT+P27dv4+fmxfv16+vY16VLt27dn8+bN7N27N8W2KlWqAHDhwgVCQkIICEgoExwWFsbx48dp06bNvfstWrTgu+++A+DZZ5+lVatWfP/99/Tt25c+ffrQunVrDhw4QK1atZg1axZ//ZVGNZxMSM9Cu3FKqfj/IoWAGzb2cQPmA//VWp+17xCFEELYXdgZ2D/f3K72hAku8xVK//E5ISCOFxdjno8QgoYNG7JmzRp27txJTEwMK1as4KWXXmLChAm8/vrr9z122rRptGrVip49e97b9vHHH1OtWjXOnDnD008/TURExL0Wzj4+PoSEhNjcFu+HH364N2Mcb8uWLYwcOZL169ezfv16XnzxRfz9/ZPsc/z4cTZv3kxkZCQNGzZk7dq1NGnShPXr1+Pj4/NAr1Fq0jNTPAWYp5QaDhwEgpVSH2mt30q0zzDMArw3lVJvApO01ilmk4UQQuQAJ9bCgqEmD7hqB9OUI5WWq7nC7P5w+zKM3uLokQiR0v1mdt3y3/9xzyJpzgwnV7t27Xu5uAEBARw/fpxu3bpRqlSpFIFncsnTJwDefPNNXFxc2LZtGx4eHnh5eREZGQlAeHg4FovF5jYAi8XCunXr+Pjjj5Occ9OmTSxdupQNGzYAcPbs2SRpHTNmzCAoKIhmzZqxePFi9u7dS9euXQkMDKR79+7s37+f8ePH88orr2TotUlLmjPFWuswrXU7rXULrfW/tNaHkgXEaK0naa0La61bWX8kIBZCiJxGa9g0Hn7rBd6lYfgaExDndlXaQ8hB2DPDVMS4EGi2x96Fvz+CVW/C0pfg95Ewd1DCDHn4Vfi+EXxVE8b7w6n1DnsKQtjLoEGDCAoKIi4ujsWLF1OnTp0HPmfv3r1Zu3YtN27coEGDBmzevBmAoKAgypcvb3MbmOC3cePGqGQfuj/77DMOHz58b6a4evXqSQL2QYMG8e9//xuAr776ipMnT9KwYUMqVarEqFGjOHXqlN0DYpA2z0IIkTdoDfOfgcN/mBq/3b4DN09Hj8o+HukOf70FS8wfURqNgNINAAUbvzQL8VzzmVk51/wQed3s5+oBxaub1+HYStgxGSq2ctCTEMI+3nnnHQYMGIDWmm7dutG2bdsHPqeLiwtDhw5lypQpjBo1iubNm3Px4kVWrlzJ9u3bUUql2AawatUqWrRoce88sbGxODs7pwiSr127RunSpYmLi0NrjYuLC7GxsSiliI6O5vPPPyc4OJi1a9fSrVs3bt++TZ8+NnKxH5DK6ppvtgQEBOjdu3dn+3WFECJP2/y1yQV+9F+5O13CltCTcPcWuHpC/iLma+f4v2/pea5/vQ3bJ8Irx8BTmreKzDty5AjVq1d39DCyVFhYGKtXr6ZFixb4+vqmui25KVOm8Msvv6QIiuNprRk7dixt27ale/fuvPHGG8yePZvmzZszZMgQwFTTGDNmDAMGDKBDhw4pzmHr9VdKBWqtA1LsnIwExUII8TC7FAR3QqHS444eSc529RjsnwePjpagWDyQ3BAU9+/fn8uXLyfZtnLlSvLly+egEdnPgwTFkj4hhBAPs6A5cHAhjD0KTulpYppHFasGbd529CiEyBZz5tipLftDRt4hhRDiYXZ2CxStKgFxesTFmNzi66ccPRKRyzniW3jx4K+7vEsKIURudWgxXDuR+uNRN+HyAdOKWaQt6ibMHQi7pzt6JDlD1E3TWOLYSkePJFfx8PAgNDRUAuNsprUmNDQUD4/Md9iU9AkhhMiNbpwz1SQKlIIR66CAjYUt53ealszlHsv+8eVGnkVNebf986DNu+Cch/9Eag2L/wXH/4IyjU2DF5Eufn5+BAcHc/XqVUcPJc/x8PDAz88v08fn4X/xQgiRix1caH4XKmO+9rfl7BZwcgG/htk3rtyuTn84tgIO/Q41eubdwHjL13B0GXT4BJr8y9GjyVVcXV2pUKGCo4chMiGP/msXQohc7sACE+wOXWVKjlks5nfiUkctx0H1bqY+r0ifqh2hQEn4/TmIvAGNRzh6RNnv1AZY+wHU6GGqccRGQ+gJKPGIo0cmRJaSnGIhhMhtom4CCmr1MUFwTBTMGwQ7pyTdzzUflK7vkCHmWi7uMGID9JwKVdqZbftmwfZJjh1XdrLEgl8j0+BFKVj6IvzSxQTHQjzEJCgWQojcxqMgjN4MDZ8z953dTEvjtR9CRKjZdnEvrHkPIq45bJi5VoESULsv+FQwubX/rII/X4fdPzl6ZNmjchsY+ie4FzD3a3Q3ta6Pr3LsuITIYhIUCyFEbqI1REeY2/Fl1pycoMPHEBMBm8abbcf+hC3fmA52IvOUMrPGVTvCsrGwb7ajR5R1/vyv6XoISdNwKrUBL1/YO9Mx4xIim0hQLIQQjnJgAQQHmtuWuIS2xPdzYQ98URlOb0y6vVg1qDMAdk2FG+fNIjvfWmZWWTwYFzfo8wtUaAFLX4DwK44eUfrExcCGL+Dc9rT33T/PtLm29dycXaBOP1OJ4naI/ccpRA4hQbEQQjhC4M+wcFhCo4hT6+DTsvBjW/jj37BtIpz8G+6GJz3u4AKT8+lbO+U5W71ufq//FIJ3SX1ie3L1gM4TzIeX5B9I4mJN3nFOCpajI2DOAFj3kVk0GHs39X1DDpu84bJNoN37tvepOxB0HBxZkjXjFSIHkKBYCCGy2+lNsPwVqPS4WeEP4FXClANz8TDNElb9F2b0gLDT5vF//jKBy4H5ppZuvkIpz1uoDPT5Gfw7Q2yU1Ce2t6KV4ZWjUKt30u27psLi0TCxCRxd4ZixJbfsZTixBho8a2pa7/rR9n7xDUvcC5j/d1JLtylWFZ5bBwHDsmrEQjiclGQTQojsFHrSVIrwqQi9pyfUwfWtBZ2+SNgv4hpcOWJaNIMJjg8thqgbUG9g6uf37wwHfzdpE2WbZN3zyKu8ipvfMZGmukdMJGz8Aso8anK65zwFj78NLf7j2HG2fhMe6Q7+naBQOfP/hS1nNsPNYBi82HYDmMTiK5mEnYWCZaR1uHjoKEe0IQwICNC7d+/O9usKIRLR2tQirT/YrLIXWc9igamtzMzd8LVQpFLGjtcaosMTqgKkdS0JWrLGn/81s/2jNpkFaVeOmrxjbz9Y/wnU7AW+Nc1/r8QL1rLD4T+gWueMNR25dQm8S6Zv3ytHYEprk2bReGTmxihENlNKBWqtA9LaT94xhcirAqfD5gnmK1aRPZycYOAieHZFxgNiMAFWegLi+GuJrFH8EQg5YBaeART3NzP/Lm7Q9l0TEAMs+Tf8/bHtjoP758PkFjCrP/z1Fpzd+uDjOrAA5g2Gfb+lfCz0JMx5Gm5egMsHzez2P9bxpzcgBijmDxWaw+p3IOSQCaiT570LkUvJu6YQedH1U7DqLajYCjwKwY7Jjh5R3uFZRDqD5Xa1+kD+ojCrL6x41fY+cbFmUd7Gz2Fae7h2IuGx3dMTFr+FnYEdU8zCyAdx47wpGefXyCyKS87J2dRb/qYO/K8p/P2RKd8XF5ux6ygFT/4Abl4w6TGY4J/w4UCIXE6CYiHyGkscLBoNTi7mj9s/f5pZI4vF0SN7uJ3dBlPbmBk7kbu5ekBD64Kz0g1s7+PsAj3+ZxavXT8Fk5vDrmkmpULHmcWSIzbA89vhzUvQyJqKEBxoe2b5fixxZqGfjoOek22nThQuD098ZpqSdJ8ELx2EYasylmYRz6s4DFoELV4zFTlK1TUtsf/8r7XbohC5kyy0EyKvObEWzm+HHpOhoJ9pSnBwgemA5pfKH3jx4Hb8D0KPp72YSeQOTV8yZfFSW8AWr0YPKNMYFv8L1rwP1btCw+HQYGii5ivO5uf6aZje0Zyz54/pD1gXjYIzm0xbZp+Kqe/XcFhCMP+gStY2P/EuBJpvnCJvQI881BJbPFRkpliIvCDkkKl5C6YBwWNjoHY/c79yG1BOZsZYZI2bwXBkKdR/Btw8HT0aYQ9u+aF6l/QtpPMuBQN/h+fWJlSvsJXz7VPBVK44tAgWDoXYaNvnu3IElr5k8nkBAoZAv5lQb1Dmnos9lG4AzcdC0Cw4utxx4xDiAchMsRAPK4sFjq8yXapObzSLg0ZvNV/9tv8oYb/8PmYm6/gqePxNx433YbZrGqDNDKHIm5ycoGiVtPdr+oL5kPrXmxATBX1/MaXfLHEmJ3jH/+D0BlPPunIb8O6ac+pRt3jNjHHpi6bEYKGyjh6REBkiM8VCPIyOLIPvG8Ds/iaHte178Ozy1Ge1qnYwv2Mis2uEeUdMpOleV60TFC7n6NGI3OCxf0OXr8wCtn2zTHD8fUNTAzn0BLR5F14+bFIxchIXN+g5xcxw/z4yfW3LhchBZKZYiIdF8pqo+Yuar2Krd029S1W8pi9Bs5ezdnx5loI2b9tuyyxEagKGQokpEIakAAAgAElEQVSa4NfQ/Luu3deUQ/PvkrnFcdmleHUYvsbkSCsl9bJFriLNO4R4WKx5H25dMCvLlVPmmgZY4swfM/Hgwq+aWb1y0lVO5FFam6oYBUqaD+gSHAsHsWvzDqWUj1KqnVKq6IMPTQhhd9dOwNbvQDknzNBk1K4f4cuq5qta8WDCzsK0dqadc/QdR49GCMewxIGLu2kSNG9Q9jf50NqMYfNXEBGavdcWuVKaQbFSqjCwDGgErFNKFUtlv2lKqW1KqbfsPEYhxP1oDStfM4tx2r2f+fMULAt3rsHZzfYbW14UEwVzn4bI69B/tqlSIERe5OwCXb6Gjp/BsRXwU0fTZCS7TO9kSuGt+8RU87DEZd+1Ra6Unpni2sBYrfXHwCqgfvIdlFI9AWetdROgolIqHUtshRB2cXQ5nFwLrd9IKPeUGRWag0u+hNavInNWvw2XD5g60GUaOno0QjiWUvDoKBgwH26chRk9sic4DTkE57aaUnGdv4RT62Hdx1l/XZGrpRkUa603aK23K6VaYGaLt9nYrRUwz3r7L6BZ8h2UUiOUUruVUruvXr36AEMWQtyjNaz/xJRba/jcg53LNR9UbGnqFWflWgNLXPrPHxMJW74xneA2fJHxTl/Z7fQm2DkFHn0eqj3h6NEIkXNUaQvDVkPn8dmzbiFojunaWbMX1B9sfjaNh+Ors/7aItdKb06xAvoBYYCtv0qewAXr7etAieQ7aK2naK0DtNYBxYrZzMAQQmSUUqYpQK8MdL+6nyrtzWzOtX8e/Fy2xMXCj23h1yfvn2sbecP8dnKB7ZNM69h1H8HU1nBpf9aMzR7KPWZKabV9z9EjESLnKe5vPngDbP0eNn+dNdexxMGB+eb9zLOI2fbEF1CkCqx+V0rFiVSlKyjWxvPAfqCbjV3CgXzW217pPa8Q4gHExZo39wIloEQN+5yz2hPQchy4F7DP+ZLb8wtc3GOaD8x5KumiPkucSQX5pStMeszMCju7moYjY3ZDv9/gdogJqm+cy5rxZVZcDIRfMTNgAUNNvVYhhG1am1bz6z9J+ABsT6c3wO1LCV07wTQt6vq1aUGdmYXIIk9Iz0K7cUqpwda7hQBb/wcHkpAyUQc4Y5fRCSFSt+17mNYeoiPsd07vUiY32buU/c4ZT2sze1OuKTw5Ec7tMLm3kWGmcsa3dWHOAAg9BY2eA0usOS6/j/ldvSs8vwP6TE/olGWx2H+cmfH3hyaQD5fUMCHSpBQ0fwVio+DgAvufv1A5eOwFqNox6fbyzaBkHftfTzw00jOjOwUYpJTaCDgDwUqpj5Lts9i6zwSgLyCNz4XIStF3TCDp7gVunvY9d0ykWWwXddO+51UKBi+BXtOg3tPw4j6zEO1CIPz1FhQsA31/hReDTCMR13wpz5HfB/w7m9sn1sCPbeBmsH3HmVHHV5u8Z/8u4CWpYUKkS8m6pjnJnhn2Pa/WULg8tP/QzA4nFxlmuu0d+9O+1xUPhfQstAvTWrfTWrfQWv9La31Ia/1Wsn1uYRbbbQdaa63t/NdUCJFE4HRTPq3Fa/Y/96X9MKuPCToz6/ZlWPy8WewSE2VqhEZHmLQC75JmnwK+5nfFx02KxJAV8MiT6c+N1hquHTer2WOjMz/WB3HrIiwaCcVrQMdPHDMGIXIjpaDeILi0z3xjlBlam3+DR5bB2g/Ne8HnFUx77NS4eZnUjQ2fSm6xSMFuub/W4Hme1vqyvc4phLAhviJD+eZZ0y3NLwDy+TxYaTbXfCavb9FI+OoRmPFkQp5wck5OmcuJrtIOev9kFgVun5j5sWaWJQ4WPmf+e/T52fbMtkhVxN1Y3lx0gAs3Ih09FOEotftCzd5mQW16RISab2Yu7jP3rx2HCdVNXfDNX0HEVZNm5ZVirX8CZ1eTunFxr1SiECnk4AbqQgibLgRCeIipOJEVnJxNwHn8r4y3fb5723TV8ygIL+43gfGuH03h/ub/MX+Q7Klqe6jWCTZ8DrX6QMHS9j3//cREQr5C0HkCFKuafdd9SMzeeY6ZO87h6uzEe93stFBU5C75faD3tNQfj4mC3T+Zmd2LexMW2DYYAqW+hiKVTFWJUnXBt1b6P5jW7g8bvzBpW+WbSYMdcY/SDvj6ICAgQO/evTvbrytErnJxn5kJiYkw7VFdPaDhcPOV35p3oe37WbeK+sACWDgMnllmmnqkR2QYzBloZn4HL0k6tqib4O6dNeO9fhomNoEnPoMGz9j//Pejtaxkz4SYOAstP1/HxZtReHu4sOONtuRzy4batSJnuvqPWXRXsra5H/9h3BIHEx+F2LtQuj6Uqgel6pvFch7eD3bNk3+bdIumLz1YJ9DscvgPE8S3+wBq9HD0aHIdpVSg1jogrf1kpliInOjgQlgwDEj0odW3FgQMM0FYuw+y9vpV2puvIA8vvn9QHBdr/rgEzYKjKyAuGnpOTRkoehTMurH6VICX9j9YN7+MCL8KS8aYHGKfCtlzzYfM8v2XuHgzihEtKjJl4ylWHLhErwZ+jh6WcASt4bde5j2iTCOTDnXjnFln4O4Fz60zv+2t0uPQ/X/mvS6ni7gGi0aDtqZsuReAym0dPaqHkgTFQuRELh5QqTV0/NQsDHHLDx6Fsm9W0sPbdJ8qmEqgYrGYGeGDC0zecD4faPAs1BuYMNuTneID4o1fmlJuFVub9q72aGiSmNaweJTpXGfPUnh5iNaaKRtPUamYJ+M6+rPmSAizd56ToDivUgoChsDa9+HmOShazbz33b1tguGsCIjj1X3K/I6LhejbkK9w1l0rNdF34PwOU2aySKWkj8W/z3oWhcF/mH1+6wVzB5n7ZRpl/3gfchIUC5GTRN8xAbB/Z5Mr68iv5guXM79vXjA5wTV6mLSKfTOhTn9o8rwpQ9ZvppltyQkNK4J3mzbV6z+B/EXMrHXlNvY7/94ZpipHpy/Bt6b9zpuHbDkRyuFLt/isVy2cnRQDGpXlo+VH+CfkNlVLZFHTGJGzNXvZBMbZ+cE/ntbwW0+ThlXc33zAz1/EfLiv09/scyEQXPODZ/GEDnmJ7ZoG109Bi/+kL7C+chT+WQkn18G57RB3FwqWhTGBCe+j0REw52l4pJtpCFSmodk+6HeY2Sdh0fLVY2aW3auEpHLZgXSeEyKnCDsL3wfA/vnmfk55g9s1FVb8B8ZXgz/HmXEVsJZVc/eC6l1yRkAMMGAOvHbKVIMoUNL88TiyzD7nvn3Z5PSVa2bSWESmTNl0iqJe7jxZ1yyK7FnfDzdnJ2btyGFdCkX2UcoEk454z1MKHh0NRSubrpRnt0Lgz3B4ScI+s/qb3OYvKsKyl02uc7y9M2H5WNNMaVZ/29e4GWzqMcfeNff3zYQ175m0iEbPmYmFp2YnvI9G3jD5zqc3mG8NE/MqDiPWQ/mm5v6iUea9+bPyMK0DLHkBguY+8MuSV8lMsRA5QUSoma2IjjC5wzlJ6zfNIjr3AlBnAJR4xNEjur/8PmZWu3JbWPUmlGmcsePPbTcLG0s8krSz36bxZjV812/MV5oiQywWzR9BF9j4z1Ve7VAND1ezsM7H042ONX35fU8wrz/hf2+7ENmm2hPmJ7G42ITbvX8y5d7ObjHVdCKumeo/pzea9QUVW0GbdxKC5XM7zAQCmEXG10+Z2z4VTTD76L/MN23xtdoT2/C5+Ubu+inoPR1qdE+5T+IPD+0/gpCDcPWomTU+stS8X9extrj+tTv0+y1r01AeIhIUC+Fo0REwq6+ZTRj8h/kKLydxdjWBYG7jXgC6fWtux8WYDoCPjrZdtik2GtDg4m5mfI4sBZSpedp8rFn13vZ9c79o5ex8FrmexaJZdegy36w9ztHLt6lWogADG5dLss9TjcqyJOgiKw5comd9yS0WOUDi9Qjxi41r9jSB7dbvzKzyrmnmw3PfGUmrYbi4gae1u2VBP/PNUqXWUNw6oRDfwCi5mEg4uhxunDUzx1XapT3O8k0TZo3vnSfK/L5yBE6tM+lvtfumfS4hJdmEcChLHMzub/JU+84wqQjC/k6sMQtU/BqZPzaeRZM+vm0i7JhkVrorJzPjcmIN7JwKd2+a/zaPdHPM2HMxrTXjFu5n3u5gKhbz5MU2VehSuxTOTirFfm3Gb8DH040Fox9z0GiFSKeoWyYIjr1rFgQmfz95ELF3zUyvrVnkjLJY4OtaJnB/ev6Dny8XS29JNvkOUAhHUk7g1xA6j5eAOCtVbgt9foHL++HHNqb+c7yom6aQv09F88ctv4/pFNjmbXj5ALR5Fw4tclwr6Vzs121nmbc7mJEtK7L65ZY8Wbd0ioAYQCnFU43KsvtsGP+E3HbASIXIgPhZYRd3+wbE8ee0R0AMJs2rVm84sdakfNhL1E2zSPBusn+rWps1HAcW5Nr3SwmKhXCUiFCTG9byNbO6WGStGt1NM5K74fBjWzi7zWzf+h1EXoe276U8xqOgSZ/oMz3nLCbMZhF3YzkbmvHycztOhfLhssO08S/OuA7+NoPhxHo1MAvuZu+UBXdC2E3tvqa+8aFFD36u2LvwW2/4vBLM6A7fN4T980wwHHnDLGye+7Rp/PRtXdj2g3m/zUUkKBbCEXZOhe8bJJ2xFFmvTEMYvsbMCrt5mooS236AGj1N3rC4JzbOwswdZ2n5xXraTdjIldtR6T724o1Inp+1h7I++fmqf12c0giIwSy461DTl4WBwUTFxKW5vxAiHUrUMIuNo25m/Njrp8ykwdoPzX0Xd7Mmo/FIswiwgC/8/hz8s8qs4dBx0PEzeHoBFK4Af70Nd6wz1Lt/guOr7fe8sogstBMiux1ZBiteNaudC0tHtGznUwGe+9vM0h9dYWqSPv6Wo0eVo+w5F8ZrC/Zz4ko4tUoX5Fr4XVYeuMwzj5VP89iomDhG/xZIVIyFOSMa4O3hmu7rDmhUlqWy4E4I+xq6Kv3l7m5dNCXpjiyDK4fMtjKNE1ra95uRsO8j3eHoMqjawTw28PeE61RpZ4LqwuXN/YhrCbdzMJkpFknduW5+RNa4fhoWj4bS9aHXNPt3XBPpE//G7V3KVKRI3kkqD7sbG8eYWXuJjI5j8qAGLPl3U/x9C7Ak6GKax2qteWvxQYKCbzK+bx0qF89YM45HK/pQoahnulIo4iyak1dz11ezQjhE/PtdZFja+67/BDZ8BvkKQYf/gxeDYNhftoNqJyezADn+seT7+FRMuN3yNdNaO4eToBjM1P+OKSbHMy8LvwKTmpoSLmBW4P/9kfnEeOO8+aQoMi82GhYMMW8cvaebznXCsUrVNfVCxT2zdpzjwo1IPu1Viw41fFFK0bVOKQLPhhEcdue+x87YfpYFgcG88HhlOtTI+GIhs+CuDLvOhHE8lQV3V25H8cO6E7T4fB1txm9IV7AuRJ63+HmY1j7tv+Ndv4WxR2HICvPemAtmd+1JpqnAWnppCqx6A/w7Qb1B5hONUx4qIh8XA/OfNfk/PtZZs8sHYNMEkycE1taXdaHr16YHu8gYSwwUqQLNX0looSxEDhJ+N5bv/z7BY5WK0Kxywqr6rrVL8cWqYyzbf4lRLW3Pqu84FcoHS83CupfaVs30GHrV9+PLVf8w7JfdFM7vyt1YC1ExcdyNtXA31sLNyBjiLJrHKhXB1Vnx7drjdK5VMs2FfELkaX4NYN9vpvtes7FQqEzSxy0W8/ffq3jqdZTzAAmKATp9AXUHmFWUQXPg8B9Q9QnTMjYz4nNvcpPV75puPT2mmNkzMKVc/DvD5YNwaZ/5uRhk+tODSdz3KOi4MecEFgvcPA8Fy9y/y1l0hFnY1Wtq9o1NiAz6afNpQiOiebVDNVSi97CyRfJTt0whluy7aDMovnQz4wvrUlPEy52X2lVh4z9XcXdxxt3FCQ9X89vd1QkfT3eerFuKSsW8WBp0kTGz97Ly4CW61C6V9smFyKvqDICL+0y76T2/Qp3+phtevsLm8cOLzGzysL+gZG3HjtWB8lbzjpBDJuCtNxAKlITF1laL8UEgmK+4/1kJrvlNonjkDZj/DNTuB488aQKb+1n9LpzZDM8szblfj8dGw8m1prtO4XLmNZk3GBqNhE6fp+8cwYHwWw/o9RNUaZu1482pom7CotFwbDl4lTClb9p/lHSfmEiTgnJ4CYzeLB8iRI51PSKaFp+v47FKRZgyOGWN+2mbT/PhssOsGduSysUTWsZGxcTRb/I2TlwJZ/HzTalSImN5xA8izqJp/9UGXJycWPli8wcKxoXIE24Gw5Zv4fpJeGquWddiiYOJTUzd/NFbHspvyaV5hy1ntpi+4l/XNv8DHJgHoSeS7uPiZoLf+PaKN86afNrFo2G8vylYnZob52Drt3BhN6x+J+ueR2ZpDYcWw8TGpota8C6z3b0AlHk0ZUB3P8Wrg7cf/D7cPO+85tpxmNIajq+Cpi9B2UeT5qSvHGe6pP2vmWkbXPlx84YjRA71w7oT3ImO5T8dqtl8vEvtkigFSxPl8Gqtedu6sG5Cv7rZGhADODspxjxehWMht/nr8OVsvbYQuVJBPzP59fQCExCHX4GFw+HaMWg17qEMiDMib/2VbjzCrKRs8SrE3YVmL5sUgfspWQfGBMKQP8G7tMm7DTtje99CZU3pk/rPwK6pjq/JF5OorujOqTCtnZn1dnaH/rMSAv8StWDggow1J3DLb0qzWOLMLHOMjRqmd8PNTPvDyL2A+dbgmaXQ7n3o+yv0mGQei7oJR5bCqv+aWfnBf0DXb8wxQuRAi/deYNrm0/QNKEPVVALbEt4eNK7gw9Kgi1wLv8vlm1FM23ya+Q+wsM4eutYpRcWinnyz9gSO+OZTiFwpPj0qaDYc+h2K14DqTzp2TDlA3kqfeFChJ2Fqa2jwLLT7IOljd28nBD0xUWY/d28Y+mf25hffvW0Csv1zzdck/95trj+zj5ndbPYy1H3afqXAji6HOQPAtxY8u8K0v9Ta5GevegNcPGDUJtM6N7eLi4W9M6D+YPNp+n6541qbGo0FSubcNBohgHVHr/Dcr7sJKF+Yn4c0wsM19ZmiWTvO8caiA0m2Pe5fnB8HBzg0dWFhYDCvzA+ijl9B8rk54+ykcFIKFyeVcNs5YZuTk8I50TZnJ4W7ixPDm1ekhLeHw56HENlOazi2wiwCL5b5BbI5XXrTJ2ShXUYUqQTPrUvZcCHqJvzQGB4bY3KUXT3MTKxnURM0bZpgvqKIDjcLrqIjoEwjaPEfc/x3DaBUfWj/YeZ7nl8INJ25jq6A2EgoVM7kQcfeNeMZMC9rgnP/ztBvJpxabwLi8CsmV/vEalOpIuSQCSSbvmj/a2eniGumnNrpjWZ1rn/n+7+eSkntW5Hj7TpznVG/BVK9pDdTBwfcNyAG6NWgNBqNxaJxcXYiv5sz7R/xdXgu75N1S7H1ZCgXb0QSZ9HExFmIs+h7PxatibWYccdpTWyc2Zb4sZuRMcRaNO92reHQ5yJEtlLK/D0TgATFGRcf6Fw/bVIp3LwgJsK0iy3bJGE/n0SB855f4E6YmTF08zQ/MYnqfZZvBvtmwz9/Qpt3IGBo2nk9WptA2Lu0KZ9y8wKc/NtU0ajdzwTdiYO2rJytrt7F/IBJl7i4Fzp9aZ7HlSOmzWRudiEQ5g6GiKvw5ER5AxEPhcMXbzH0512ULpSPn4c0pEA6Os+5uzjzdOOcV07QxdmJ8X3rPNA5np+5h8V7L/DfJ6rj5pK3MguFEIakT2TW7ummyUX4VQgPMYvz2r5re9/0lGgLPQnLXzHnrN4taSvF5O7ehulPmDrCrd4wyfFxMeY6GckLzgphZ0zlDq/iSbdfPwXRd0yAHB0Bd2+ZHurx5WDiYnNmd7cDC8wiSy9f898kcaUSIXKps6ER9Jq0DVdnxYLRj1G6UD5HD8nh1h27wpDpu5j0dH2eqJV367QmFx1rkQ8JIteT9ImsFjDE/KRHemZpi1SCQYtg7Qdw9agJcp1Tmbk5utwExO0/hvqDzLbU9s1utrrfWCwwe4ApAaMtYIk12wOGQpevzGK9/ysFnsVMibhCZc1PpTZQ1tpz3RKX9UHz9VNmQWLENajU2sy6F65gGrk8ORE8i2Tt9YXIBiG3ohg4bQdxFgtzRjSRgNiqRZVi+Hp7MD8wWIJiq/PX79Bj4haGNavI6FaSDiYefmlGGUqpgsAcwBmIAPppraOT7VMYmAkUBwK11iOzYKwPP6VSn21O7NAiUw6tyfO5o0mIkxP0nmZm1929zOywu7cp6wYQFw3NXoKws6a82+lNcOsCOLuZoPj2JfiqpkkVKVQ2IXD272wW+FniTOCc2aD5xnnY+DnsnQlOLuBdCopUNo/5NYABc+3zOgjhYDfvxDB42k6uh0cz67lHqVxcKqLEc3ZS9Kxfmv9tOEnIrag8v+AuNs7Ci3P2ci08mqmbTjGkafk0c86FyO3SE0U8DUzQWq9WSk0COgJLku0zCJiptZ6plJqllArQWufy/AgHu3XJdqvFqJsmd7jhc7kjII5XogZ0/tL2Y675oPUbSbfFRpu2yABOrtB8rAmYw86aWtG3L5nFhL614MIemN4xIWguZA2aa/cBn4pm1l05wZ3rcHQZHF4Mwbth3FkTSG/60nQybDjcXCezix2FyMHuRMcy5OednL4WwfQhDalTppCjh5Tj9G7gx8T1J/l9z4U8PzP63d8n2HPuBgMfLctv28+xZN9F+jYsk/aBQuRiaQbFWuuJie4WA67Y2C0UqKmUKgSUAc7bZ3h51MHfTaWDf+2A4v5JH3Nyhc4ToHQDx4wtu7i4Adb8aK9i8PhbSR+PvWtmh8GkNTR90QTNN87BiTUQfhnKPWaC4qPLTHFybTE/PpWgzlMJ56rRA5r/J2UveCEeEtGxFkb/tod9528w8en6NK1c1NFDypEqFvOiYfnCzA88z6iWFZO0us5Ldp25znd/H6dn/dJ8+GRNdp8J46ctp+kT4JdnXxORN6T7+2alVBOgsNZ6u42HNwOdgReAI8B1G8ePAEYAlC1bNlODzTPKNTUzmwfmQ5u3kz7mlj8hjzgvc3FPuO1T0VTtSCwmKqGCR5HKplyes7upklGiZtJZ9oqtsnq0QjiMxaJ5ZX4QG/65yme9atGxpuTL3k+fBmV4beF+9pwLo0G5h6C+egbdjIzhpTn78Cucnw+erIlSiqFNK/Dawv1sOxXKY5XkA5V4eKVrSalSygf4Dhiayi7vAqO01h8AR4EUK9C01lO01gFa64BixYpldrx5Q4ESUKGlCYoTVweJugk7piRtJyxsc/VIWHzoWwvavget/2tuy0yHyEPm7j7P0qCLjOvoT7+GMiGRlk61S5LfzZlJ60/luQ55WmveWnyQy7ei+KZ/XbzczbxZt7ql8PF0Y/qWM44doBBZLM2gWCnlBswH/qu1PpvKboWBWkopZ6AxkLfeSbJCrT5w4ywE70rYduxPWPkqhJ5w3LiEELnKkn0XqVzcK8/nyKaXl7sLL7apwpojISwJuujo4WSr3/dcYGnQRV5uW4V6ZQvf2+7h6syARmVZcySEc6F37nMGIXK39MwUDwPqA28qpdYrpd5VSn2UbJ9PgCnATcAHmG3fYeZB1buar/v3z0vYdngxFCgFfg0dNy4hRK5xLfwuO06H0qmmLB7NiOHNK1KvbCHeXXKIK7ejHD2cbHHmWgTv/HGQRhV8GN2qcorHBzUph7NSfL7qKNGxFgeMUIisl2ZQrLWepLUurLVuZf15X2v9VrJ9dmqta2itvbTW7bTW4Vk35DzCwxv6/QYtXzP3t02EE2uhRndT4kwIIdLw16EQLBqpu5tBzk6KL3rX4U50HG8tOpir0yjOX79DZHTcffeJibPw4tx9ODspvu5XF2cbbbtLeHvwr9aVWbb/En0nb+P8dZkxFg8fad6Rk1Vtb36HX4VV/zW3a/Zy3HiEELnKyoOXqFDUE39fqUecUZWLe/FKu6p8svIoi/ddoEc9P0cPKUNuR8Xw5apj/Lr9LK2qFuOnZxumWjni6zX/EGStTFLqPs1cxrarir9vAcYt2E/nbzfxbNMKhIbf5dz1O9yNsdCrQWmerFta6hmLXEumHHMDr2Lw2mlTos0vzS6FQog84petZ1h7JMTmY2ER0Ww9GcoTNX2ljFYmDW9ekQblCvPq/P3M2J7akpqcZ+WBS7SdsIFft58loFxh1h27yp8HL9vcd/upUCauP0nfAD86peMbhU61SrL8heZUKOrJt2uPs/zAJW5FxnAjMppxCw/Q5JO1fLHqKFdu5Y20E/FwUY74WiggIEDv3i29PYQQIrNOX4ug9ZfrAXiipi/vd6tB8URd2ObtOs9rC/ezbEwzapYu6KBR5n63okyJsr+PXuGpRmV5v1sN3Fxy5nyS1pqv1hzn27XHeaSkN//XsxY1S3nT7fstXI+IZs0rLe9VlAC4cSeaJ77ZhIerM8vGNMPTPf1fHmutiYiOu3c+rTXbT11n+pbTrD4SgquTE70alOa55hWpWMzL7s9ViIxQSgVqrdOcVcyZ/7KFEELc19KgiygFo1pWYu3RK7QZv4FZO87dy39dcfASZXzyUaOUt4NHmrt5e7gydXAA/2pVidk7z/Hs9J1YLDkvx9hi0bzzxyG+XXucPg38WPLvptQtUwgXZyc+7lGTkNtRfLX6n3v7a615Y9EBrt6+yzf962YoIAZQSiUJsJVSNKlUhCmDA1j3Sit6B/ixcM8F2kzYwKgZgew7f8Nuz1WIrCI5xUIIkctorVkSdJGG5X14/Ql/+jUswxu/H+CNRQdYdegyb3auzpYT1xjatIKkTtiBs5PitY7+lCzowdt/HGLp/os8Wbe0o4d1T2R0HK8t3M/SoIuMbFGR15/wT/LfvV7ZwgxoVJbpW05Tr2whrt2+y/ZT1/nz0GVef8Kf2n72bfldvqgn/9ejFi+3rcrPW08zY9tZ/jx0mUcr+jCyZSVaVS0m/1+KHEnSJ4QQIpc5evkWHZ+I20YAACAASURBVL/exIfdazLo0XKAmSmcsf0sn6w8QkycJs6iWfy8mS0U9mGxaDp/t5mIu7GsGdvS4WkUsXEWFu4JZsLqfwi5dZfXn/BnVEvb9ahv3omhzYT1XAuPBqCEtzsda/jybtcaONmoNmFP4XdjmbPzHD9uOs3lW1H4+xZgVMtKdK5dEldn+cJaZL30pk9IUCyEELnMF6uO8r8Np9jxRhuKerkneez0tQhenR9E+N1YVr7YXGbk7GzdsSsMmb6LD5+swaAm5QE4eTWcj5YdplmVYjzVqAz53bL2S1itNX8fvcKnK49y/Eo49coW4o1O1WlY/v5tqY9cusWZaxHULVuIkgVTrzKRVaJjLSwJusjkDSc5fiWcxhV8mDPi0Sz9fzQsIppC+V3l30EeJ0GxEEI8hLTWtPxiPeWK5GfGsMap7mex6CyfAcyLtNb0m7KdU1cj2PhaK05djWDwTzsJvxtLdKyFwvldefaxCjzzWDkK5XdL9TxbT1xj0oaTeLq5UMTLDV9vD56sW5qyRfLf9/r7zt/g/1YcYefp61Qo6slrHarRMZdVGLFYNFM3neKTlUeZMqgB7WtkTXOZdceuMOznXVQtUYDBTcrTvV6pLP/AInImCYqFEOIhFHT+Bk/+sIXPe9emb0AZRw8nTwo8G0avSVvpWqcU649ewTufKzOGNSLsTjST1p9kzZEreLo5M6BxWYY3r0iJRFVBwAS2A6Zux9vDlQIeLoRGRBN2JxonpehSuySjW1XC3zfpAskz1yL4YtUxlh+4RFEvN15sW5X+Dcvk2vSD2DgL7b7aiLuLEyteaG73D3BhEdF0+Hojnu4u5HN15vClWxTwcOGrvnVp+0gJu15L5HzpDYrlI5MQQuQiS4Mu4uqs6JBFs2sibQ3KFabdIyVYGnSRSsU8mTGs8b2mFz8+48PRy7eYtP4k0zaf5petZ+nVoDQjW1SifFFPTlwJZ8j0nRT1cmfB6CYUL2AC5ss3o5i2+RQzd/w/e/cdHlWVPnD8eya9Q3pCgNBLKNK7UgRREQvIghVRVFQUG6usqwKLris/14odkS5gXYoISpdQQi+hBUgnjXSSTDm/P2YSkxAgwKRA3s/zzJPJnXPvPXOH8t4z73lPLD/vSaRtiDf9WvjTs5kfG46kMj/yNE4OBp4b1ILxNzYtU/nhWuToYGDSzS14bvEelu9PYnjHULse/58/HyAjr4jZY7sREepN1OmzvPbTAV79cT89mvri5epk1/OJ64OMFAshRA0xWzS5BSaKzBaMtkeRyWL7XZf93WTdNm35Qdo3qMdXD8tCPjUpLiOfb7ac4ukBzfArl9ddLDY9n883nmBpVDwms4Xb2oewOzaTQpOZ7yf0prGfx3n7ZOYXsXhHHOuiU9gVexajWeNgUPytW0MmDWpRphb1tc5i0dz6wSaMZgu/PX8jjnYa9f5lbyLPLtrNS0Na8szAFiXb98ZlctesLTzapwmvDWtrl3OJa4OkTwghRC2WXWBkxKw/OZaSe9n7fnp/Z26txOpjonZIySlg9uZTzLetirf48Z6VWlAlr9BE1OmzNPR1p4n/+QH09WD1wWSemBfFf0Z2YGTnMNLyCikyWQirf/Hc6vJMZgsnUvM4mJjF1P8doom/B8ue7HVeoP3qD/tYsjOeVc/1o2WQLH9eV0hQLIQQtdjz3+3hl72JPH9zC3zcnHByMODsaMDJwWB7rnB2cMDJQeHkaMDZtt3d2YGw+m7X1MQqYZVdYKSgyHxdjfZeLa01d36yheikHCxaY7ItjHJ3pwa8cUfbCicrnisyE52czcFE6+NQYhbRyTkUmiwA+Hk4s/TJXhWupJeRV8SAmetpG+LNwvE95O9RHSE5xUIIUUv9sjeRH3cnMOnmFmW+3hXXN29XJ7wll7UMpRRv3d2eeVtP4+/lTJC3K0lZBXy5MYZNx9J4c3hb6rs7czAxqyQIjknNpXhRQW9XRyJCfXiwZ2PahnoTEepDswCPC6Zi+Ho489ItrfjnTwf4ZW/tWoRF1DwZKRZCiGqUkHmOoe9vpHmgJ0ufOP/rXSEEHEzM4uWl+ziUlF2yLcTHlbYh3kSEetM21IeIUO8r+tbEbNHcPWsLx87k8s0j3ejZ1M/e3Re1jKRPCCFELWO2aO7/KpJ98Vmseq5fhROthBBWRrOF1QeT8XFzom2I9wUnNF6J1JxCxnwZSWLmOeY80p3uTS6+8Im4tlU2KJYhCiHqsOJZ2mZL9d8c10UfrD1KZEwGbw6PkIBYiEtwcjAwrEMo/VoE2DUgBgjwcmHh+B4E+7jyyDfb2RaTbtfji2uTBMVC1FHHU3J4eeleftmbyKZjqTXdneveH9Fn+PCP49zbJYx7u4TVdHeEqPMCvVxZNL4ngd6u/O2LSMZ8Ecn/9iZSZJuwJ+oeCYqFqIMKTWaeXbQHDxdHfD2cWbQ9tqa7dF3JzC9iw9FUss4ZAWtN20mL9xAR6s30u9rJjHchaokgb1d+mNCbl29pRdzZfCYu2k2vt3/n7VWHOZ2eV9PdA6DIZOHT9Sfo9fbvrItOqenuXNckp1iIOujtlYf5fGMMXz7UlZ2nMvhq80n+fGXgecvRisu3O/YsTy/YRWJWAY4GRbdwX9JyCzmTXcDyif1o5Hd59VeFENXDbNFsOpbKwm2x/B6dgtmi6dvcn/t6NGJw26AaWVJ787E0Xv/lADGpeXi7WguGrXi2Hw195d+RyyEl2YQQFJksZBcYyT5nJLvARPY5IzGpuXyxKYb7bf/QNw/05PONMSzdGSflwa6C1pr5kaeZtvwQQd6ufHJfZw4kZvHH4RROpuXxxUNdJCAWohZzMCj6twqkf6tAkrMK+G5HHN/tiOWpBbvw93RhVNcwnripGT5uVVtWz2S28NuhM8z58xTbT2bQ2M+db8Z2o1mAJ8M+2sSEBVEse7I3rk4OVdqPukhGioW4DhxPyWHV/mQ2HU/jbF6RLRA2cc5orrB9qyAvfnq6D27O1n9U7/syktPp+WycPAAHg3y1f7nOFZmZ8uN+ftydwIBWAfz3bzeUWXSg0GTGxVH+AxPiWmO2aNYfSWHR9lj+iE6ha2Nf5j3W3a5/n7XWxGbksycuk92xmaw+mExSVgFh9d0Y2zucB3o2LgmA1x46w2NzdzKme0PevqeD3fpwvZORYiHqgKjTZ/n79/s4blsquGOYD80CPPFxc8LbzdG6WECp59btTjTydS8zyjCmeyMmLtrNpmOp9G8VWKV9/nF3PL4eLtzUMuCSbfMKTfy4O4Gfdifg6epIi0BPWgR60b91AIFetSPV42RaHhPmR3HkTA4vDG7JMwOaYyh3YyEBsRDXJgeDYlCbIAa1CSqp1jPlhwPMvLfDVc8NMJkt/G9fIh//cZwTqdb8ZTcnB7o18WXq8AgGtQk6b5Di5rZBPNW/GbPWn8Bo1tzfoxE3NKwn8xTsRIJiIa5ReYUmnlu8G61h2p0R3BIRfMU5wbdEBONnm3BXlUHxybQ8Xlq6DwelWDi+B13DK64NGpOay7zI0yzbGU9OoYnWwV7kFZnZeiKdQpMFHzcnZtzdjmEdQqusr5Wx+mAyLy3Zi4ODYs4j3SsV6Ashrk3DO4YSk5rL+2uP0TzQkwn9m13RcYxmCz/uTuCTdcc5nZ5P62Avpt8ZQefG9WkV5HXJBX1eGNyS3EITS3fGsywqnhaBnozq2pC7OjUgwMu+pevqGkmfEOIa9eYvB/l26ymWPtHrgsHl5Xh75WG+2nySra8MJLCKJtw9u2g3aw6dIcjbhZwCEz893adkwkjx15Tfbj3NxqOpODkobm8fwkO9w+lkGwkxWzTRydlM+fEAe+MyueuGUKbe2a7Kc/zKM5ktvPvbET7fEEOHMB9m3d+ZsPqSLyzE9U5rzXOL9/DL3kRGdQ2jvrszbs4OdGlcn34tLn5TXGSysCwqnlnrjxN/9hwRod5MHNiCIW2Dzvt2qTJyCoys2JfEkp1x7IrNxNGgGNg6kFFdG9K/VYCsllmKrGgnxHVsx6kM7v1sK2N7h/Pm8Ai7HPNEai6D/m8D/7itDeNvbGqXY5YWnZzNrR9s4smbmjGySxh3f7KF0HpufPVwV1buT2Je5GniMs4R5O3C/T0aM6Z7owuOepjMFmatP8EHvx+jsZ87S5/oZffi/heSmlPIs4t2szUmnft6NOKNO9pKeoQQdUiB0cwzC3cTdTqD/CIzhba6xs8ObM6km1ueF+AWGM0s2RnHZ+tPkJhVQMeG9Xh2YHMGtg60W9rD8ZQclu6M5/tdCaTlFuLv6cKIzg0Y3b0RTfxloSC7BcVKKR9gMeAA5AF/01oXXaDtLGCV1vp/FzumBMVCXLkCo5nbPthEkdnC6kk34uFivyyouz7ZQoHRzK+TbrzqYxWZLDg7/jVSMX7uTiJj0tk0eQD13J3ZdCyVsd/sKFlNr3sTXx7uFc6QiMqXPoqMSefh2dtpGeTFwvE98HK98hHj5fsSmb35JAalMBgU3q6ODGoTxNCIYOp7OBN/Np/F2+NYtD2W3EITM+5uz0hZhEOIOq/AaOb1nw+wZGc8t0QE8d6oGwA4lZ7H1hPpfLExhpScQro0rs+zg1pwYwv/KssBNpotrD+SypKdcfwRnYKzg4F1L/Un2Ofqv/3TWrM/IYulO+P5IzqFiFBv7urUgIGtA3F1ckBrTV6RGTcnh1o3YdueQfFTwDGt9Rql1KdYg95fKmjXD3hea33PpU4qQbEQVya7wMirP+xnxb4kFjzWgz7N/e16/HlbT/HPnw+y8tl+tA31vuLjbDiaymPf7qBfiwCeG9QCjTXgfnFwSyYO+qvs20+7E9gde5bR3RvRJuTKzvdH9BkenxtFt3Bfvnmk2xWVKSo0mbnxP+twUIomAR6YLZqkrAJOp+fjaFC0DvHiYGI2ChjQKpCXbml1xf0VQlx/tNbM3nKKGSsO4eLoUKbyT8+mvjw7sAW9mvlV64S4Y2dyGPzfjUy6uQWTbm55xcdJyy3kp90JLN0Zz5EzObg4Gujb3J99CVmk5hTi5eKIt5sTabmFFJoshPu5M/PejnZJ67OXKkmfUEotA2ZqrSPLbXcC9gMrgQ1a658vdhwJioW4fJuOpfL3ZftIzi7gxSGteHpAc7uf42xeEd3fWsvDvcJ5bVjbKzpGclYBt324CXdnB3ILTWTmG6nn7oRBKTZOHoCnHUe2i/20O4FJ3+2hW3h9BrYOolmAB62DvStdF3jR9lhe/WE/8x/tQd8W1hsNrTUHE7NZvi+JbSfT6dfcn791b0SDem52778Q4vqw5Xgay/clEVbfjXA/D1oGedIiyKvG+vPQ7O0cTc5h898HXFaOsdFsYV10Ckuj4lkXnYLJormhYT3u7RrGsA6h+Lg5YbZo/jyRxsr9SRSaLPh7uuDj5sTiHbHEnz3HY32b8OKQVrWinrLdg2KlVC/gX1rrQRW89ihwO/AUMBFI1lp/VK7N48DjAI0aNepy+vTpSp1XiLrOZLYwffkhvt16mmYBHsy8tyOdGtWvsvM9MW8nUacziXx14GVP1DCZLdz31TYOJGTxyzN9CfJ2Ye7W08zdeopJN7dkTPdGVdNprIHt+2uPcia7sGRb9ya+PNI7nMFtgy74XkxmC4Pe24CPmxM/P91HShsJIa4bvx1M5vF5UXz2QBeGtgu+ZPvEzHPM3nySn/YkkJZbRICXC/d0asDILmGVDu5zC028tfIwC7fF4ubkUCaNbvWkG+2SynG57BoUK6V8gd+AEVrr86JZpdTHwHKt9a9KqTbAjIulUdTESLHWmlnrT3Bru2CaBnhW67mFuFL5RSaeWbibP6JTGNenCZOHVv1d9+qDyTwxL4pvHunGgMssz/beb0f48I/jvDeqI/d0rpl825wCIzGpeWw7mV4yeS/Ux5UHe4UzultD6ns4l2lfXHv0swc6M7RdSI30WQghqoLJbKHff9bRPNCTeY/2uGhbrTW3fbiZY2dyuLlNEPd2DeOmlldexWLL8TTWHDpTZtvzN7fEx716qwWBHRfvUEo5A0uBVysKiG2OA8XT1bsCtW4Y+FhKLv9dc5R3Vx+hZ1Nf7uvRmKERwWXuYISoTdJyC3l0zg72J2Qx4+523N+jcbWcd0CrQOq5O/HDroRKBcUms4XtpzJYtT+Z+dtOM7JLWI0FxABerk50bFiPjg3r8WjfpvwRncKcP0/yzq/RvL/2KHfd0ICxfcJpE+KN1ppP15+gWYAHQ9peehRFCCGuJY4OBsZ0b8R7a45yMi3vopUofj+cwuGkbGbe29Euk4j7NPe3+7yXqlaZiXYTgLeAvbZN6wAnrfVrpdp4AbOBIMAJGKm1TrjQMWsqpzglu4ClUfEs2m7Nd2no68ZLQ1pxR4fQK6oRKERVScg8x/1fRpKcXcBHYzozuG1QtZ7/9Z8P8N2OOHa8djPeFVR0KDCa2XI8jV8PJLP28BnO5htxdTIwpG0w/x7RHnfn2rcu0JHkHL7deoofdsVTYLTQo4kv3cJ9+Xjdcd4d2YF7uzas6S4KIYTdpWQX0PvffzC294XnimituXvWn6TlFrLupf6VrgB0rZA6xRdhsWjWH01h5uqjHErKJiLUmzfuiKB7k9ozU1LUXfFn8xnzZSSZ+UbmPNKdLo2rLn/4QvbGZXLnJ1to5OvOo32bcG/XMCwa1kWn8OvBZNZHp5BXZMbL1ZFBrQMZ2i6YG1sG1MpguLzM/CKW7Izj2z9Pk5B5jgb13Fj/8vX3n4AQQhR7esEuNh9PY+urAyv8d3rL8TTu/2ob/7qrHQ/0rJ5vJauTBMWVYLFoftmbyLurj5CcXcBrt7dhbO9wmWgjakxcRj6jv4gkp8DI/Md60CGsXo315ffDZ/hk3XF2xWbi5epIodFCkdmCv6czg9sGM7RdML2a+l2zKUhmi2bD0RSCvd2uqvycEELUdsULPrUO9uKT+zvTrNzcqtFfbCUmNY+NkwfUimoR9iZB8WXILTTx/Hd7WHPoDH/r2pBpd0XIClWi2p1Oz2PMF5HkFZlZ8FgP2jXwqekuARB1OoMF22Kp7+7M0HbBdG5Uv9YVZhdCCHFx66JTeGHJHgpNFt66uz13dWoAWP+NH/HpVl67vQ2P9bP/aqa1gQTFl8li0fx37VE++uM4XRvX59MHulxwiVkh7O1UWh5jvoykwGhm/mM9iAitHQGxEEKI60dS1jmeXbSbHafO4uHsQJC3K3lFJopMFra8UnFqxfVAguIrtHxfIi8t3YuvuzNfPNS11ozWVZdzRdZVeNycZaS8usSk5jLmy0iMZs2Cx3rISmlCCCGqjMlsYWlUPMdTcknOLiA1u5BR3Rpe18vWS1B8FQ4kZPH43J1k5Bcx896ODOsQWtNdqlJxGfn8fvgMv0ensC0mA5PFQri/B22CvWkT4kWbEG9ah3gT6uMq+dZ2djzFGhBbLJqF43vSKrjmVj4SQgghrkcSFF+l1JxCJsyPYufps0wc2Jznb255XZVtyy00sXJ/Esui4tl+MgOApgEeDGwViIeLI4eTsolOziE2I79kHx83J1oHW4PktqHeDG4TdN5CCKLyjp3JYcyX2wBYNL5HjS4FKoQQQlyv7LZ4R10V4OXCgvE9eP2ng3z0x3Gik3P4799uwNPl2rhkFosm85wRk9mCn6cLDgaFxaKJjElnWVQ8qw4kc85opqm/By/f0orb2odUWNQ7p8DI0TM5HErKsQbKSdks2RlHfpEZZwcDgyOCGNW14TVdhaAmHEnO4b4vIzEYFIvG96R5oKyyKIQQQtQkGSm+BK013/55iukrDtMswIOF43vi73nhCXiLt8ey8/RZ3hnRoUpn6B9IyOJIcg5puYWk5RaSnltEam4hablFpOcWkpFXhMli/WwdDIpALxfMFk1KTiFero4M6xDKyC5hdG5U77JTIiwWzaGkbL7fFc+PuxPIzDfi4migY1g9uoTXJ9j7r3XN2zXwqZE6u7XZkeQcxnwZiZODYuH4nueVxhFCCCGE/Uj6hJ1tPpbGQ7O38fiNzXjl1tYVtknNKeSmd9eRX2Tm5Vta8fSA5lXSl+LyKcVcHA34e7rg7+WCv4ez7bn1p6ODgZTsApKyCjhnNHNLRDBD2gbZrQ5hocnM+iOpbD+ZQdTpsxxMzMJo/uvPlEHBlNva8GjfJpKPDGTlG7n9o00YzRYWP97roktuCiGEEOLqSfqEnfVt4c+QtsEs3hHLpJtbVBhUfrLuOIUmC72b+fHfNUfp18L/kosvpGQXcCAxi5ZBXoTVd69UX95feww/D2eWPNmLIG9XPJwdaizgdHF04JaIYG6JCAasy//m2ypYmMwWXv/5IP9acZgTqXlMuzOiTq8aZrFoXliyhzPZBSx5QgJiIYQQojaRoPgyPNw7nF8PJvPLnkRGdWtY5rX4s/ks3BbLyM5hTLmtDUM/2MikxXtY/mzfkrp/eYUm9idksTcukz22R1JWAQDuzg5MHR7ByC5hFw1wd8WeZdOxNF69tXWt/Nrd1cmhzA3DrPs7M/O3I8xaf4ITqbm8PqxtnStzV+zTDSf4PTqFqcMj6NRIUkqEEEKI2kSC4svQs6kvrYK8mPPnKe7tWjZ4fX/tMVDw3M0t8HF34v9GdeT+r7bx7KLd+Hu6sCcuk6NncrCl+dLI152u4b50DPOhVbAXn6w7zsvL9rHpWBr/ursd3q5OFfbhg7XH8PVw5sFe18ba5AaDYvJQawA/9X8HGfbRZm6JCOKxfk0pNFqIzcgnMfMcTg4GvFwdbQ8nvG0/S2+rjRP5fj98hh92J9Am2Ivezf3p0MAHxwpGw/88nsb//XaEOzqG8tA18tkJIYQQdYkExZdBKcVDvRvzjx8PEHX6LF3DfQFraa0fdsUzrk8TQuu5AdC7mT+P39iUzzfEUM/diY5h9RgSEUynhvXoEOaDX7nJer2b+fPp+uP8d+0xdp7KYNqd7bi5bVCZNnviMtlwNJW/D219za06M6JLGIMjgpi9+SRfbzrJ6oNnSl5zMCjMlkvntrs4GkoFzH8Fzd4lwbPTRQNrHzenCgPWKxGXkc/U/x1i7eEz1Hd3YsW+JPjtKF4ujky+tTUP9GhUctO09UQ6j8+LommAJ/++p73kVgshhBC1kEy0u0z5RSZ6vPU7N7UM4OP7OnM8JZeXl+3l2JlcNk4egG+pur1aa5KzCwj2rvyiF7tiz/Lq9/s5ciaHWyKCeO32toTVd0MpxSPfbGdPXCab/z4Qj2ukNFxFMvOL2HgsjQBPFxr5uRPs7YrWmtxCEzkFJrILjOQUmGwPY5mf2QVGsi/wWnEu84V4ODvQs6kf/Vr4M6B1II39Lj+nt9Bk5suNMXy87jgGpXhuUAvG9W1CToGJyJh0Fm2PZdOxNEZ1DWPane3YdCyNpxfuorGvO3Mf7U6Ij9uVXjYhhBBCXAGpPlGFpi8/xLd/nuLOGxrw4+543JwcmH5XO+7pbJ8lEo1mC19tOskHvx+lwGjBwaDwcXMiI6+oSqtaXOtMZssFA+vsc0aOpeSy+Xgap9PzMSh4sGdjXhjSCh+3ilNVytt0LJU3fj5ITFoet7YL5p/D2pZ8M1DMYtG8v/YoH/5xnOaBnpxMy6NdAx/mjO0mC50IIYQQNUCC4ip0Oj2P/jPX42hQ3N+jMRMHNj8vHcIe4jLy+e3QGTLzi8jMN6LRvHprm2t6lLg2iMvI5+vNJ5m79RS+Hi784/bWDO/Y4IJ1pZOzCpi+4hAr9iUR7ufO1DvbcVPLgIueY/XBZF5cspcbGtbjswe7XDOLvgghhBDXGwmKq9i2mHRCfNxo5Fe5Mmqi9jmQkMU/fjrA3rhMgrxduKtTA0Z0DqOpvweODgaMZgtztpzi/bVHMVk0Tw9ozuM3Nq10jef8IhOujg7X1fLgQgghxLVGgmIhKsFs0aw5lMyyqHjWHUktmfDnYFA4GBRFJgsDWwfy5h0RcgMkhBBCXINk8Q4hKsHBoBjaLoSh7UJIzSlk9cFkMvKKKDSZKTRa6NXMj4GtA6VihBBCCHGdk6BYCJsALxce6Ck1hIUQQoi6qPathiCEEEIIIUQ1k6BYCCGEEELUeRIUCyGEEEKIOk+CYiGEEEIIUedJUCyEEEIIIeq8GqlTrJRKBU5X0+n8gbRqOpe4OPksqp5c49pDPouaJde/9pDPoubItbdqrLW++FK01FBQXJ2UUjsrU7BZVD35LKqeXOPaQz6LmiXXv/aQz6LmyLW/PJI+IYQQQggh6jwJioUQQgghRJ1XF4LiL2q6A6KEfBZVT65x7SGfRc2S6197yGdRc+TaX4brPqdYCCGEEEKIS6kLI8VCCCGEEEJclATFQgghhBCizpOgWAghhBBC1HkSFAshhBBCiDpPgmIhhBBCCFHnSVAshBBCCCHqPAmKhRBCCCFEnSdBsRBCCCGEqPMkKBZCCCGEEHWeBMVCCCGEEKLOk6BYCCHENUUppapzPyFE3SBBsRBCXCWlVGyp5x5KqQ1KKVc7Hv9xpdTd9jpeJc8ZUcG2xkqpPy/zOF2UUqGXaOOslNqmlPKsxPEMwFKlVODl9MPmhyvcTwhRB0hQLISoEkqp+5VS39iev6aU+qTUa+8ppaZdZN+xSqlzSqnkUo+XlFJvKqVylVIpSqkEpdSLpfZ50tZup1KqSant02ztf1dK+dq2/Vzu2EVKqRttr21VSu1VSu25yOPjcl1OLfX8QaAV8KtSar1SamKpvgQrpQpKnXeJbft6pdRZpVS6UmqeUsqn3PH7Ao4XuFYOSikn23NnpdQS2zlOKaVuLtXmS6VUqlJqqVLKxbb9lO2cZ5RSHyqlnG3bXYEVSqk5SimPUqcrAowV9MFQqg9KKeVSalR2AnBTufaNlVJ/2t73euA3oB2wqXibUmqLUmp8BW/5BWCz1jrF1q5/RdflAt4CvryM9kKIukRrLQ95yEMedn0Aw4BMYI7tdz/gDOANuANJQPBF9h8LLK5g+RBXAQAAIABJREFU+5vAv23Pw4EUoCPQHkgAQoFewApbm9uB/bbzjgFmVXBMX+AI4Gr73QVQwHBgXgXtFeBkex4B9AYOAzcCgcA2wBXroMNRoEmpfYcCn1dwzPW213yA/wFLyr3+u+197KzgsQd4xdbuVWABUA94CDhm2/40sAZwtrWZbNt+CmgNhNj6/Z9S5/QG5gBNbdd5D3AAyLU9TwPetLXtCOyz9SfK1q6J7bXPgDvKvR8HoDPgWOpzfQ5wL3WNbyn+vdR+PsAGwFDquvW/zD+bnwADavrviDzkIY/a96hw5EEIIa7SI8B0rMEqWut0pdSPWIPdfOBXrXXy1ZxAa31KKRWJdVS2FTBXa50IJCql/G0jnPcAH2uts5VSi4EpFRxqEtZgucB23EIApVRXrMFf+fNq/hotvRdrMNwAeMe27WOtdYFSajiwSWt9stTunSo6ZqljZymlngGOK6U8tda5tpeaAJ211pmXuCx7be8lSym1HPjctv0erAFvkVJqFrAS+E+p8yYppSYD3wGTlVI3AFla67G2a5Gvtb5BKRWM9Walv1JqNfCnbf+9QIfSHVFKjVdKdcE6yt1AKXUL0FFr3Q/QwAzggG20vB8wAvhdKTXddj2fA4Zg/fNS7HZgqdbaconrcDFfAE8B667iGEKI65CkTwghqsJIIL3ctv9i/Sr9advzq6KUagR0xTrKG4Z1pLJYItC49PbiYLZ0OoAtXeB+4OsKTtEFeLxUysQhpVTpABet9Zta64FYR4R7Y00DmKmU2gwsBloppWKVUkNtu3QCXralc0QqpVqWP6nW+jTW0djmpfroXYmAGK31Sq11lu3Xm4FI2/PS1yELCKhg9z1AkFKqHtAS2KaUeuoip7sD6wg2SqlVtnSIXbafO7GO8j4J7ALeBSYD/rY+WGz7fwe8gfUmYx/wjdZ6JWACetluckrrCGwvt+0BpdRppdQOpVRTW3/WK6XmK6XilFJRpa+zLYA/77oLIYQExUIIu7MFoOW3HQGOA2la633n73WeO0vl3pbOP35GKZUCHAPetQU5DkB2qTZ5WFMIym/Px/oVfLERwG+lRmRL6wL01lrfoLW+AXgUiL5AX12A14BlwCqtdV8gwfZzbql2McBIrXWgre17FzheLlA86SwIOGXLla7okaOUKp+z6wJM5a/R6/LXoaJvCYuvgafWegnWIP+UbZvBds23APWUUoeBI1prM4DW+las6R++wI1a665a6w9s+/pjTbXwKnUOsKaePAc8gDWd5nmgSCn1PtYbmqlKKfdyfawPnC23LQDrSPpC4INS211tx5kDfFhuH6lCIYQ4jwTFQojqFGV7VMbPWutg2+P1Uts/xppPnAussG07izUILuYGWC6yvdiDwKLyJ1ZKhQN5WuvSo90tKBcUK6V6KOtkwhBgB2X/TXUu9dyglHLQWr+itd5l2zYLGFj+3DYetveH1jrOFmRW+ADisN4glDYN2KO1/tX2e/nrUFFljOIR9OLzHgfClFLeQA7WNJMFthuED7GO8pY2BusNQKBSammp7a2Ak1hzlHMBlFJ9sAaxM4B5QDDWCXDtsKZM/IQ1J71/uXOkY81PL222beR5AdCn1Pa5tu2LgJ7FG20TAM+7aRNCCMkpFkJcc7TW+Uqp2VhzQydhneDVD1hgC3o6Y514txPrxLsttlHHJtjSOmwVHjpiHf0sLwJwUkqVzv8NAV4v184f+BZr7nQK8BFgsaUPNFJK7bed7yYgTynVWGtdPHLcADCXqtKArV9hWEeJjymlxmBNPcjj/EDOy/begrCOtBbvfyvWXOfOpdoWX4dlylqZo3SebrEOQGJxmoYtH3gy1kmE8VgnAL6ulPoAeBYoKRFnS/GYgDVl4yzWHOIhWFMi4rXW55RSpUeKd2JNj8jGmkaBUioe64S712w52Xspe2MB1huq3vyVFkKp62Kh7E1J8XU1UPZGqBtwsIL3L4So4yQoFkJcqz4Gdiml/gGsAt5TSv2BdbQxXWsdp5RaBqxXSu3DGsT9obUuniQ3CNhanAJQmtZ6BdCo9Dal1E+UGym2tUMphW0EuLis2ydALGDWWt9b6hjRylrTeA/wT+AXrbUujotto7IfAou01nlYRznPG8kudbwQ23vVtt+bYx2tvb1cDvIi4HOlVCrWm4ifyh0nGGuqxVe2372wjuCOx5of/pPWOkcptRBrYLpca136WkzBesPxO3AO643Aq1iD1+IR5dJB8UdAW6WUqdQxArB+jmbb9VBYb0yGlEpvWQWsVkp9qLUu3vch26TCMVgraBQbq5T6n2176drKE/hrAqIQQpSQoFgIcU3SWp9WSm0E7tdaf6GUGol1VLUQ6+Q5tNYnbJPF3sI6cvxEqUPcRNkRR5S11q6popxorKXLoovblQquwTYqacvlfRtresddwCdKqc+A57XW54BxWEeWfYFfgYmljrEI6wSz77HW4r0g2yTDjljTLw6XeulprDnTv5QagO6ktd6olJqJNed2H9Z842JbsNYf/gZr2gVYKzSsx1p/+S6sAawH1pJ37ljziptprU/Y2n+JNfiNLhWgP2rrTy+lVADW1IZUAK314xW8p3jg1gvkd2PbL08pNRfriH3pUftYrCP1Y0ptO4s1JzoFGG07x2DAWWtd5nMXQggAVfG//UIIUffYAsfhlP26vSJOWCtJABTnz87AWsViITBda21SSjnyV+5wN611+UliV9rPYGA21sobH2itT9njuKWO3wFrQNkPa9pIINbA8mus72cc1tSKOOCW4jJ2pfavD/wB3KO1PqmUWgcUAM9qrcvnPxfvcwZoXZlrpJT6COs1TrnA6+ux1lBeX27718AkrXXOpc4hhKh7JCgWQtQYpdQOoGEFLzUuH2jVVsq6HHI01goPflrrpAraNNRax1V75+zAFtgPxZp6kl9quwFocKH3pZQyXGU94St2oaBYCCEuRoJiIYQQQghR50lJNiGEEEIIUedJUCyEEEIIIeq8Gqk+4e/vr8PDw2vi1EIIIYQQog6JiopK01pXtLx9GTUSFIeHh7Nz586aOLUQQgghhKhDlFKnK9NO0ieEEEIIIUSdJ0GxEEIIIYSo8yQoFkIIIYQQdZ4s8yyEEEIIUQsYjUbi4+MpKCio6a5ck1xdXQkLC8PJyemK9pegWAghhBCiFoiPj8fLy4vw8HCUUjXdnWuK1pr09HTi4+Np0qTJFR1D0ieEEELUKlGnM1i+L7GmuyFEtSsoKMDPz08C4iuglMLPz++qRtklKBZCCFGrLNwWx/Tlh2q6G0LUCAmIr9zVXju7BMVKKUelVKxSar3t0d4exxVCCFG3ZOYX8f2ueM5kF5JbaKrp7ggh7KyoqKimu3BB9hop7gAs0lr3tz322+m4Qggh6pDT6fklz0+l5dVgT4QQF/LZZ58RFxdXZtvNN99cqX2fffZZ/vzzzzLbjEZjyfMPPviAxYsXl/xuMlXfzbG9Jtr1BIYppQYA+4EntNZyiy+EEOKyxJ39Kyg+kZpLuwY+NdgbIeoWk8lE06ZNadq0KQB+fn4cOnQIT09P7rjjDl5//XUAWrZsyYgRI3j//feZMmUKAPv376d///4ALFy4EG9vbwYPHoyHhwcmk4khQ4YwZcoUHB0d8fLyKnPe3r174+bmhsFgIDY2lkaNGvHZZ5+htSY/P5+NGzfi5uZW5e/fXkHxDuBmrXWSUmoucBvwS+kGSqnHgccBGjVqZKfTCiGEuJ7EZZwreX5SRoqFqFb79u1jzJgxvPPOOwC8+eab3H333YwYMYLevXvTqVMnBgwYQMuWLYmMjGTr1q3079+fN998k2HDhrF8+XIiIiIIDg7GYDCwdevWkmPHxMQwZswY9uzZQ0JCAoWFhTz22GPcc889DB06lO7du5OWlsaJEydo2rQpDRs2JCEhgRMnTlRLQAz2C4r3aa0Lbc93Ai3KN9BafwF8AdC1a1dtp/MKIYS4jsSdzae+uxNLn+xNWP3q+Y9QiNrqb59vPW/bsA4hPNgrnHNFZsZ+s/2810d2CePerg3JyCtiwvyoMq9990Svi54vMjKS5cuXs27dOtq3b09ISAgAbm5ujBo1ig0bNtCkSRPuvvtuZs6ciZ+f33nHWLt2LQaDgX//+9/8/vvvmM1mHBwc8Pb25vvvv+exxx7j9ddfZ8WKFSUT46ZMmcKpU6eYPn06eXl5NG/enO+++46VK1fy5ptvVvZyXTV75RTPU0p1VEo5AHcBe+10XCGEEHWI2axpEehF80BPXJ0caro7QtQp3bp1Y+3atWzfvh2j0cjKlStLXvPz8yMzM5N27dqxbt06AgMDOXfuHLNnzyYkJITc3Fw6dOjAjBkzAHjllVf4+uuv8fHxYeXKlSxduhSAtLQ0AgICyMnJwdPTE4Do6Gi+/fZb3nnnHTp16sRLL73E+PHjWbduHd9++221vX97jRRPAxYCCvhFa73WTscVQghRh7wzsgMAO05lsPbQGV65tbWUqBJ11sVGdt2cHS76uq+H8yVHhsvr0KEDLi4uAHTt2pUff/yx5LWMjAx8fX0BCAsL44MPPqB9+/ZMnz6d+fPns2bNGiZPnswdd9xRss/cuXN56qmn+Prrr3Fzc+Phhx8mLy8PNzc3cnJy8PDwICsri88//5wZM2bw2GOPkZWVxcyZM3FycuJf//oXixYtoqioCGdn58t6L1fCLiPFWusDWusOWuv2Wut/2OOYQggh6q5Didl8vjGG1JzCSzcWQtjFgw8+yN69ezGbzfz000907NgRgMLCQpYtW8bgwYMB+PrrrwFYvXo1Xbp04aabbuKtt95i27Zt9O3bF4ATJ06wcOFCYmNjOXLkCNOmTWPfvn0lxyweKf7+++85efIkjz76KFprUlNTSUxMxGQy8dxzz7Fp0ybmzp1bLe9flnkWQghRK6TkFPDcoj1MHNScJv4eAMSk5RHo7VrDPROibnj99de577770FozfPhwnJycmDFjBh999BEPPPAAgwcPZtu2bcyaNYv58+fzxBNP0K5dO9zc3GjZsiUff/xxyTc7ZrOZkSNHEhwczIABA7jxxhuZOHEis2fPBiA5ORlvb2/GjRvHuHHjSvrw/vvvExwczOjRo6v9/UtQLIQQolY4nZ7P1ph0nripKc0DrbmGMal59Gx6/mQeIYT9tWvXjn379pXZVn6im6enJ7NmzWLFihU8+eSTvPHGG+zbt48///yTr776ir59+/LSSy9x1113MW3atJL9lixZwrhx42jUqBEDBgygSZMmhIeHn9eHwsLCaq1NXJoExUIIIWqFuAxrjeKGvu6E+rjh7GjgZFpuDfdKCFFaREQEAD169ODo0aM0btyYqVOnlmxLTk6uMP938uTJGAzWrN1169Zd8Ph///vfq6DXlWOv6hNCCCHEVSmuUdygnhsGg6KJn4fkFNdSyVkFfLkxpqa7IWpYy5Yt6dOnT5ltwcHBJRPySisOiGszGSkWQghRK8SdzSfI26WkFNvPz/SRsmy1yM97EkjKKuDJm5oRdzafmb8d4Y6OoQT7SM63uD7U/rBdCCFEneDr4Uz3Jn/lD0tAXLu8t+Yo8yNPAxDg6UKhycJPexJquFdC2I8ExUIIISrlcFI2M1cfsUtKg9bnL2w65bY2fDSmU8nvBxKyeHrBrpJcY1FzTGYLCWfPMbxjKADh/h50aVyf76PiK/wshbgWSVAshBCiUg4kZPHxuuOcKzJf9bEWbo9l5Kd/kpVvvGCbQpOFFfuTOHom56rPJ65OUlYBJoumka97ybYRncM4lpLL/oSsGuyZuB4UFRXVdBcACYqFEEJUUtY5awA79X8Hr+o4WmvmR8aSV2TGw8UBo9lCXEY+/d9dx/ojKSXtmtpqFZ9My7uq84mrVzxaXzoovr1DCM6OBpbvS6qpbolawmKx8MEHH5Cenl6yzWw2M3To0ErtP3z4cBITE8tsK12W7cUXX+TPP/8s+d1ovPDN9NWQiXZCCCEqpTgo/j06hYTMczSo53bR9oUmMy6O1rxgrXVJUf/dcZkcTsrmyZua0fvff/CP29vg7+nCqfR8nB3/Gqup7+FMfXcnTqRKUFzTzhnNNKjnRsNSQbGPmxPfP9mb1iFeNdgzYU9ZWVmMHj0as9mMh4cH3333HRMmTODQoUPcfvvtvPbaa4wdO5Y9e/bg6OjIE088wfjx4zEYDISEhDBq1CheeOEF3n33XQD2799P//79cXBw4Ndff+XkyZOMGzcOV1dXioqKeOSRR3jkkUdwdHTEy+uvP0dJSUnceeeduLm5oZQiNjaWqKgowBqAu7i4sGbNGru/fwmKhRBCVEpxUAyw+Vgqf+vW6IJto5OzeXTOTqbfFUF0cg6HErP5+L7OAMyPPI2HswNPD2jGiv2JLNoey103NACgYX33Msdp4u8htYprgUFtghjUJui87e3DfMr8nl1gZH98Fr2b+ZXcBIkrtOoVSN5v32MGt4db/33BlxcsWMALL7zA4MGDmTBhAosXL8ZsNrN161bGjRvHsWPHAPj4449p06YNHTt2pEePHgQFBTF48GBGjhzJokWLGDt2LGPHjmXYsGF8//339OzZEycnJ1q2bMnmzZtLzhcZGcn999/P3r17eeqpp0hISODtt9+me/fudOvWjdGjR7NlyxY8PT05d+4cAwcOZO3atWUCaHuSoFgIIUSlZJ8z0tjPnQKjmU3H0i4YFEfGpDN+7k7cnR0I8XEjOjmH5fuSuLdrKh3DfFi+L4lRXcPwcnVidLdGvLv6CH4eLjgYFCHlynu1b+DDaZloV6t9tSmGffFZuDs7sHhHHACLxvekVzNZifBa89RTT5U8T01NZf78+UyaNAmAIUOGlAlo/fz8uP3229m4cSNt2rThqaeeYvbs2edNvHR2dmbVqlUATJo0iUOHDmEymTAYDLRq1YoFCxYwZMgQ5s6dyyuvvIKzszNKKaZPn05MTAzLli0jIyODoUOH0qFDB06ePMnIkSOr5P1LUCyEEKJS/vu3GygwWvjHT/tZF52CxaIxGMqOBq7Yl8Tz3+2hkZ87347rToN6bjQN8GDpznim/nKQH5/uwz9vb1OydPO9XcJ4b81RVuxPIqy+G44OZae6TL2zXbW9P3FhzyzcRRN/D14c0uq8185kF/DL3kTcnBy464ZQ7ugYSvcm5y/eIC7TRUZ0q9rWrVs5e/Ys4eHhNGhg/RbH19eXXbt2lWnn5+dHZmYmgwYN4ocffkApxaFDh5g+fTpTp06lcePGtGrViscff5yXXnqJ999/n02bNrFs2TLeeecdXF3/uglWSpGTk4Onp3WJ9127drF7925efvllNm/eTN++fZk6dSpHjx7F3d2d2267ze7vWybaCSGEqBSlFG7ODgxqHUS7Bj6czS87Y3xvXCbPLNpFhzAflj3ZqyTn2MXRgdeHtSUmLY/F22N5sFc4LYKsX38GersysHUgADdX8PW8qB22HE8jPa/iCgETB7XgwzGd2PaPQbw/uhOD2gThYJDUiWtVRkYGEydOZPbs2SVpCwC5ublYLJbz2havXteyZUvmzp1LSkoKn332GR07dmT9+vV06tSJUaNGlewzb948nnrqKd588002bNiA0WjEyckJgJycHDw8PIiJieHXX39l9OjRLFiwgG3btrFy5Uqio6P5+OOP2b/fzmklNhIUCyGEqJT//BrNj7vjub1DCPMe7YGfp0uZ1zuE+fDW3e2Z/1gP6rk7l3ltQOtAujfx5Zstpygwli3p9uzAFsx/tAevD2t73jnPZBdwz6wtrD6YbP83JColu8DI2XxjmcoTpXm7OjG8YyjertbAZu7WU3wfFV+NPRT2UlRUxL333svbb79N48aN6dKlS0nKxN69ewkPDy9pm5mZyapVqxg4cCAA06ZNo2HDhqxdu5ZevXoREBDAe++9h8VioVEja6rVtm3biIqKYv369SQlJfHGG2+wZs0abr31VoCSkeLFixdz8OBBJk6ciMFgID09nVOnTmEymXj88cfZsGEDy5cvt/v7l/QJIYQQlbJ4Rxy3tgvm7k5hAOQVmnByMPCvFYd4qFdjmgd6Mab7hSfffTD6BuZtPU35+VflJ2uV5uPmVFKt4paIYLu8D3F5isuxNb5AUFzej7sTcHd2YESXsKrslqgCX3/9Nbt27WLGjBnMmDGDRx55hHnz5pGYmMiqVauIjIzkt99+Y+LEibi4uPDOO+/QunVrfv75ZyIjI0tGf+vXr899993HwIEDWblyZcnxLRYLI0aMoGnTptx6662sXbuWd955h59++gmw5jF7eHgwZcqUMv2aNGkSo0ePpmfPnlX6/iUoFkIIcUlaa7LOGannbh0N/HrzSf7zazTdwn3ZfDyNZgGeNA+8+IzwEB83Jg9tfVnndXVyIKy+GzFSlq3GFAfFDSsZFIf6uHEoKbsquySqyIQJE5gwYUKZbcOHD2fNmjVMnjwZHx8f5syZc95+Pj4+fP7553z66ac8+OCDvPDCC6SkpLB9+3beffdd3n33XaZPn06fPn3o1atXyX67du3i9ddfx2Kx0L17dwYPHoyDw/nLuxcWFpapW1xVJCgWQghxSbmFJswWjY+bNShuEehJocnC1ph0/jOiA6O6Nayyczfx95QFPGqQi6MDXRvXr3xQXM+VtYfPlKlNLa5d9evXL5MTXJH+/fsD8O677xIVFcXYsWPp0KEDAEuWLCEmJobQ0NDz9vvwww8xGKyZvNu3b7/g8T/99NMr7P3lkaBYCCHEJRXXKK7nZs0V7tHUl1Fdw7i1fQgDWgVW6bmb+nuw9FSGBFk1ZEDrQAa0rvxnHFrPjUKThbP5Rnw9nC+9gyjjWv9z3qVLl/O2NW3atMK2xQGxvZQvB3e5JCgWQghxSXmFZjycHfC2jRS7ODrwn5Edq+XcnRvXJyHzHOeMZtyd5b+t2i7Exw0HgyIlp0CC4svk6upKeno6fn6y+Mnl0lqTnp5epszb5VJXG1Vfia5du+qdO3dW+3mFEEJcnWt9FEtcvmEfbaJ3M3+m3NamUu2NZgsGpaQs2xUwGo3Ex8dTUFBQ0125Jrm6uhIWFlZS4q2YUipKa931UvvLLbcQQohKq8mAWALy6me2aI4k59CvRUCl93FykGqvV8rJyYkmTZrUdDfqLPmTK4QQ4pI2HE3l2UW7S3KLq5PWmoEz1/PWysPVfu66Ljm7AKNZX7BG8YVM+98hlu6Mq6JeCVE17BoUK6WClFK77XlMIYQQNe9wUja/7E3EsQa+EldK4exo4ISUZat2senWcmyXGxT/EX2GjcfSqqJLQlQZe48UzwTc7HxMIYQQNSzrnBEnB4W78/k1RKtD0wAPKctWA4prFF9uUBzi40Zi5rmq6JIQVcZuQbFSaiCQB8hanEIIcZ3JzDfi4+ZUYzm9Tfw9iM3Ix2i21Mj566qQeq7c0TGUEJ/Lm9EfUs+VJAmKxTXGLkGxUsoZ+CfwykXaPK6U2qmU2pmammqP0wohhKgm2eeMJeXYakJTf0/MFk2sbeRSVI9+LQL4aEwnHC9z8lyDem4kZxdgkpsYcQ2x10jxK8AsrXXmhRporb/QWnfVWncNCKj8LFYhhBA1z8XJcNlfodtThzAfHu7VGOfqrGyQsAu+HAgLR0MNlC+tDfKLrmxp3Yb13Qn2duVsfvVPzBTiStmlTrFSaiNQfDt4A7BMa/3YhdpLnWIhhBC1VmEO/DEDtn8OTu5QlAt3fw4dR9d0z6pdl+lruKNjKG8Oj6jprghxxSpbp9gut9xa6xu11v211v2BPRcLiIUQQogrYTRbSM8trOKTFMCXg2DbZ9B1HDx/AMK6wep/QH5G2bYWM2TGwaktcOgXKMyt2r5Vs/TcQtLzimhQT+bPi7rB7t9D2QJjIYQQ15FH5+zgux2xNdqH+7/cxlMLdlXtSSI/gbQjMGYR3P5/4FYfhv0Xzp2F36dZ2xgLYM0bMCME3m8Hc26DJQ/CrF5wbE3V9q8aHUzMBiAi1Puy9y0yWRg3ZwfLouLt3S0hqoysaCeEEIL4s/l4uTjh437+ZDqzRfN7dAoRDXxqoGd/aeznzvqjVThROzsJNv4ftB4GrW79a3twe+g5AbZ+DCEdYNvnkBoN7e+Fxn2gXiPQFuto8oKR0G4EDP03eAZWXV+rwYHELAAiQi//c3d2NLDjVAYN67sxskuYvbsmRJWQoFgIIQTPLd6DRWt+fKrPea/lFFgnS9WrweoTAM0CPVkaFc/ds7YQEerNP25ri5s96yb/PhUsRhgy/fzX+r8CB36A5c+DVyjcvwxaDC7bpsmNsPl92DQTjv8Ot8yAG+6Ha3Rp6oOJ2TT0davwRqkyQn3cSMgssHOvhKg6ssyzEELUYWaLdbL1kLZB7I7NZFtM+nltMm0VBHxqOCi+p3MDHu3bBGcHA+uPpOLqZMf/wuJ3wt5F0Otp8G16/usuXjByNvSZBE9tPT8gBnB0gf5/hyc3Q2Ab+PlpmDsc0k/Yr5/VaHjHUJ4Z0PyK9w+t50pSltQqFtcOGSkWQog6asW+JD7feIK547rzcO9wvtwUw8frjtOjqV+ZdlnnakdQHOjlyj+HtQVAa/3XQiLGc7D8BciKA4MDGBzB4GR97uBk+73Uo2Sbg62dI0SvAM8g6PfihTvQuJf1cSkBrWDsStg1x5p7/GlvuGky9H7Weu5rxC0RwVe1f2g9N/bGZ9mpN0JUPQmKhRCiDvpxdzwvLtlL50b1cTAoXJ0ceLRvU975NZq9cZl0bFivTPvWwV4EeLnUUG/PZ7Zoxny5lTtvaMAD6R/B3oXQqBeYi8BiArPRWh3CYrT9brL+tJhs28y2NrbfHVzgns+tI8L2YDBYq1e0vBVWvWydpLf/e7jzY2jQ2T7nqEIpOQWk5hTSKsjrshfuKNY62Ivo5BxMZssVH0OI6mSXOsWXS+oUCyFEzVm0PZYpP+6nZxM/vnq4Kx4u1vGnXQ+MAAAgAElEQVSRnAIjff79ByO6hPHGHTVYl7YgG86etI4AFz9Mtp/OntDmDjA40OHN1fy96Snuj5kMvZ6x5vBeCa2tD0MVBm6Hl8PKl8BUCC8cBqfLWza5us3efJJpyw+xfcogAr1rd1+FuJTK1imWkWIhhKhDftgVz6s/7OemlgF8/mAXXJ3+mqjm5erET0/3IdzPo+Y6qLW1xFny/gu3CesGd86ipUc+w0+/BUHtYdDrV35Opap+MlybYeDsAfPugujl0H5k1Z7vKh1MzCbAy0UCYlGnSFAshBB1SO9m/jzcqzFTbm+Di+P5lRuaBniet23BttMsi4pn6RO9qv5r8FObrQFx3+chvJ91RTknV+tPR1eIjYRVk+GzvvxXBeNszocRX1knudV2TW6ylm/bNfcaCIqzrqg+cWmpOYU8+PU2JvRvxp03NLBTz4SoOpLkU0VMZgtfbz7JuDk7qn4FJiFqgfVHUnhm4S5mbz5JXEZ+TXdHlKK15tcDyZgtmmAfV6be2a7CgLjYou2xDJy5vqQyxam0PKKTcqonL3TbZ+DmCzf9HZoPsk5sC+1knbxWvzF0/Bs8vR2a30xD02k+cX4EAltXfb/swWCATg/CyQ2QcbKme3NBBUYzx1JyaXcF9YlL83Zz5MiZHE6m5dmpZ0JULQmKq8ip9DzeXnmYdUdSeGzuTgqM5prukhBVQmvNJ+uO88icHWw4ksq05Yd4ccnemu6WsNFa8+7qIzw5P4rvd1VudTFXJwMxaXkcPZMDWKtPVEvlicxYOLISOj8EThdZWtgrCEYvYFGfVRxpOKrq+2VPN9wHygB7FtR0Ty7oSHIOZoumXYOrGyl2cXTA39OFxEwpyyauDZI+UUWaB3qx8rl+xKTmMmHBLp7/bg+f3NcZg+HaLOIuxIWsO5LCu6uPMLxjKO+M6MCZ7AKybYs9iJq3cn8ys9af4L4ejRjZuXIri3Vp5AtA1OmztAnxJjO/moLiHV9bf3Z79NJtlWLM4N6Mqdoe2Z9PGDQbBLsXQP9XrWXhaplmgZ58O647HeywgmGojytJWbKAh7g2yEixnT3/3R7GzdkBQMsgL4a2C+Eft7Vh1YFkIisoii9EbZVbaLro60azBYABrQL5Zmw3Phh9A27ODoT7e9AhrB5ztpxk4f+zd9bhUV1bH37PzGTibsQ9QAjurgWKVijUaUv91l1u3duvt/fWaKmXCvVS2kKB4u6uUUhCXCcyGTnfHzsJCfHMmVjnfZ48CTPn7LNDZuasvfZav9/OM+0xVRtNsDe1AAc7FS/MjW/xojzEyxEfF3v2pRYAVZniNrqatRhDOez7AnrOEHW33ZlB10FJhnC964S42GsYH+uLp7PW4rECPRxtmWIbXQZbUKwwybmlNcFCNYvGRLDirjGMivbpoFnZsNE60grKiH/mL/6z+mSDz286lcOENzaQkK1DkiQm9vI7b6RQxZrjWSzbbQuKO5rT2SVE+7mgbsUulSRJDAr1YN8ZERTH+Lsw4ALdYsU5/COUF8Dw21p8yoGzhYx+dR17UvKtODErEHsxOPnA/i87eiYN8v3usxw4W6jIWMMjvBgY6qnIWDZsWBtb+YTCZBdXEOVbN/iVJIm+wWIbaltiLnqDmYm9/DpiejZstIgtp3MBeHtdAsMivBkTI17Tsizz0eYkXl15glh/V7RNNF71CXTn860pGExm7GzC/R1GuLczfduwDT6jbwBBno6YzDIvXtLXCjOrhSzDriXgFycUJ1qIo52a9MJyMou72Pa8Rgv9rxRNhZlHoEd8R8+oBoPJzL+XH2HhyDBFFkI3jI5QYFY2bLQPtjuVgpjNMtklevzdGpYGkmWZN1ef4s6v93HYZn1poxNz+eBgvrl5OLH+Ltz97T7SCsoorzRx77IDvPznCS6OD+DnO0cR6u3U6Bh9At2oNJlJyNa148xtXMgLl8TzyPTWqzNcMjCIZ2b3aVWGuc1kHYHMQ8IBrhV6wdUOe9nFXVDhZ/R9Ilv8w0LQl3T0bGpIzNFRaTQTr0A9cW06wijMho3WYguKFSS/rBKjWca/EbFzSZJYfO0gvJy13PTFbtIKbLJVNjondmoVo6J9+PC6IahVEkfSi1myKYkVhzJ4dHov3r16IE7apjea+lTJOR1Jty0AOwpLAxGDyUxaQRmjX13Ht7usWApz6DtQaaDPZa06zcPRDju1RE5XlL108YV5n0J+Eqy4V2TLOwFH0osBLNYoruZkZgn9n1vN38ezFRnPhg1rYguKFcRslrlsYBC9Axr/MPFzdeCzG4dSYTBx0+e7KSq3denb6Fyk5Jby/IpjpBWUEeHjzKZHJjI9vge3T4hk2S0juGNCVL364YaI8HHG08mO/NLKdpi1jYb4YW8aw15aS1Ybywuu+Xgn13+yi/TCcowX9EoohtkEh3+C6IvA2btVp6pUEj4u9l0zUwwQPhomPQVHfoI9n3T0bABh2uFopybCp76JS1vwdLajqNzAuSJbs52Nzo8tKFYQPzcH/rNgAMMivJo8LtbflQ+vHUxybqmtO99Gp2PT6Rw+3ZqMuSoGqs4I22vUDI9sedCiVkns+fdF3DY+yhrTtNECErJ1FJYb8HFpm9tb3yB3kqqMF9ysJcmWskUoMfS7ok2nXxwfQJxCWc0OYfR9EDMVVj0OP98GB78DXcdlVU+cK6F3gKtiZTM+zvbYqSXSC7tY3beNRjGZZUq6qeymrdFOQYwmM2qV1KIs2qhoH365czRxTWSVbdjoCLYl5BHk4UiIVxPmCS2kXepRbTTK6awSonxbpzxRm8FhnnyyRTivtVmnOHkzZB0VqhINfTYe/h60rkKRoQ08PTuubfPqLKhUcOmH8NcTcHo1HFoGSDDnbWFi0s58uWgYBQru7qhUEj3cHWyybN0AvdHEr/vT+XBjEkPDvXhtXr+OnpLi2DLFCvLe+kT6PPNXPUm2xogPckelkjibX8aKgxlWnp0NG81jNsvsSM5jVJR3ixZ3zbH/TAHzFm+z2bx2EKezdcT4tX0bfFAtKS0Pp1Zq1ppNsOE1+GI2rHoUtr9X/xhDORz7DXrPBm3jTZvN0eWbuJy84NIP4KEEuHUjhI2Cv57skIyxnVqFXyN9MW0l0N3RVj7RhSmpMLBkUyLjXl/Poz8dxlGrZkJP346ellWwBcUKklVSgaOdutXyU/cs299i+1UbNqzJ8cxiCssMjIpuXW1nY2g1KvakFnDY1mzX7pTqjaQVlFsUFPdwPx8c+bm2ogSjNBe+ngcbXoZ+86HXLFjzFCRtrHvcqVWgLxbHtJElmxLp+dQq69U8tycqFQQOgNlviwXD6qeaPr6iGA4ug6/nw3/ioMiy+8i2xFye/e0oRWXKbo3P6BvAhJ42GdKuyltrTvPynyeI9nNh6aJh/H73GPoEupOa1/2SHYqWT0iS5AUMBvbLspyr5NhdgeziijatsAPcHThxrvNI8tj455JZVIGPi5aRkcoYzcT4CS3joxlFzOkfqMiYNlpGpdHMjaPDGRFl2QJn8TWD8HW1J9CjheU0qdvhx5ugLA9m/w8GLYRKHXw8BX68EW7dcN6x7tAP4NIDIsa1eX5OWg2VRjN5pZWNKv90OXyiYfQ9sPlNUUIRPvr8c5WlcOov0Zx3eg2Y9ODiD7osSFwv3PLayKZTuXy9M5UnZvRW4Jc4z8JR4YqOZ8O6nMkrY8nmRC4ZEMSQcC9uHhvB3AGB9K+lW33TF7uJ8XNh8bWDO3CmyqNYUCxJkifwO/AH8B9JkibJspyj1PhdgewSfeuyKVWEeDmx9lg2JrNsq8G00aFM7u3P7ienKFI6ASJTHNvDhaNVMk822g9PZy3PzO5j8TgX9w1o2YGyDNvegbXPiqD35jUQ0F88Z+8KC76GjybC0suEWUVFESRvguG3g0rd5vlVaxXnlOi7T1AMMPYhOPQ9/PkQLFoDSRvg6M9wciUYykQgPPgGiL8cgofA65GQtsuioPhoRpEw5dEov4lcVmnETq2yGfl0cpZuT+GZ346iUamI9XdlSLgXgR6O9RbF/m72bVa16cwo+ersBzwgy/JLwF/AIAXH7hJkFVc0atzRFKFeTlSazN3yBWaj61Bdl6lUQFxNfKA7RzKKun7dZxcjV6dHbzQpO2h5gdjSz0+u//iyq0WJRK8ZcNvG8wFxNT7RMO8zkE3Cxa2iCKImwdCbLZpSdSIiu6SbfX5qnWD6q5B9DF6PgO+uEZngfgtg4Qp44DjMeB1Ch4tFRfBQOLu7zZeTZZmjGcXEBypr2gGw4WQ2cU//xSGbaVWn58/DmUT4OLPl0YlcPzK80eP8XB3ILumiUohNoFimWJbljQCSJI0DhgHPKzV2V2HBkJA2SQOFeokGk9S8spZvUdqwoTAH04q4+9t9vHvVoDrbZJYyItKbXJ2eskoTzvY2wZv24rGfDpFWUM6q+9pemlCP/V/Dtrdhz2cw+7/Qdx5k7Ifvr4fiDBHEDb+9cVe6mCkQs1+5+VA3U9zt6DUTRt4F5YUQfylEjAd1IyogIcMgYY041rH1799zRRXkl1bSJ6iRe5gsw5qn4cwOcPQUzYHBQ1vkQlhdmy6a7TybPNZGx1JYbiDCx6XZUlA/N6EPLsuy4omUjkTpmmIJWAAUAIYLnrsVuBUgNDRUyct2Gh6Y2rNN5w0I8eCnO0bRq4erwjOyYaPlJOfqOJtfjouDsoHrJQODuGRgkKJj2mieU1k6+gYrnPU78Qd4x4CTN/y0CA5+K0ognP3gxlUQMlTZ67UAX1d7rh4eSri3c7tf2+pIEkx7qWXHhgwT39P3QPSUVl8qq7iCHm4ONU6U9dj2jlgQBQ4SutKZh8Tf/+wumPMOaBpXJwlwF8kemyxb58fVQUOQR/NlSP6uDlSazBSWGfB0bqUyTSdG0bufLPZH/yVJ0gvAHOC7Ws8tAZYADBkypNvto1YYTOgNZtwcNa1eNbk62DE4zLZ6ttGx6CqMALg5WMekobtlFDoz5ZUmzhaUcdkgBRcjuhw4uwPGPyrqXTe+BpveEAHYZUtE5rADsNeoefnSvh1y7U5F0GCQVKKEog1B8cBQT3Y8MbnhMqfkTbD2Geg9B+Z/KYJ1WRZ///UvQXE6LFgqMsgN4OagwcVeQ4bNwKPT8/1tI1t03ISevvi5DcTBru39AJ0RJRvtHgXOybL8JeABFCo1dldgy+lcbv5yD8v/NbpNW89rj2VhNJuZHt/CphYbNhSmuCoodlU4Uwww8+3NDAjx4CVb8NIuJObokGXhnqkYp1aBbIaeM0CtgUlPCkMOJ+9mt8+tjcksU1pptNqCrktg7wp+caLZzgLqLVyL0uCHG8E7Gi55//zfWpJg/CPgEQbL/wXvDgVnX/EaUdlB38th8I3g6IEkSQR6ONi0irsRkb4uRPoqYwXemVCy0W4JcJ0kSZsANbBawbE7PVlVTR5t7X7+fFsKizcmKTklGzZahU5vxE4tYW+FznNJgrQC2w2xvUjI1gFYpFFcjxN/gHso9Ki1sHH26fCAGGDhp7u44VPLgsFuQfBQSNtLjUd7K5j/4XY+23pBA6XJCN8vBKNeqIfYN7DI6r8Arl8uDEe8o8C3pzhu7bPwVh9hX114hhtHRzCzn02WsTOTp9Nz9Uc72HSqeeEwg8nMtsRczuSVtcPM2g/F7n6yLBfIsnyRLMvjZFm+U/6HtZpnFeuRJPBxaVttTYiXE2e6oRC2ja5DhLcz0/r0sEqJQ4C7I5lFtq3T9iI+yJ3HL+5FmFJ1tnodJK4TjV+dIAi+EB8XLTm6btho11pChoO+CHJPtuq0/NJKdiXn13dj3fOpqFGe/V/wjW18gPDRoqxiwVfi+00r4bbN4vWyawn8bwBXnXmOOb7t79Bno+XklVayLTGP4ormzVuMJpmrP9rJikPdy43X1gquENnFFfi42KNpowZjmLcTBWUGiisM/+wtQBsdxvyhIcwfGmKVsQPdHdiZlGeVsW3UJ9rPhWgls8SJ64RJRK+Zyo2pIH5uDuSUdL9O+FZT3Wx3dif4tdyA42iGkEqrI8emy4Z1L0LkRKGF3FoC+ola88lPw84PkPd8hnTkR8zh41CNvkfUPUsS5JyCPZ9A+l6hvewWJEo1Bi8ETeslTi3CqBcqKkVpok665nu6+F6aK7Shxz8q3Ae7GYVVToYejs0n9xy1atwcNGR3MylZW1CsEG3VKK6mWpbtbH5Z492/Nmx0UXq4O1JcYaRUb7TJslmZ7OIK9p0pZEJPX+WaYE78IZqoQlvWhNPe+LrYU2EwU6L/h9cVe0WKGu+zu0Xw1kKOVJnr1JEUXfOMMAmZ8YZluwPuwTD1RZa7XcPRFW/zaM56VF/PA9/eovwmZbOoQQ4eCnmJkLxZZLuPLYcrv2q0ec9iyvIhZYsIxtP3Qs5JKG0gk+3oBe5B4B4igvaNrwrljUs/BIfWS7B2ZgrLKgHwcGrZe8jfzYGs4u61Q2O7OynEFUNCqDC0XSi/Oig+k2cLim10DDd+tgsHO7VVbDsHhnpw7YjQ+tuzNhRnxaFzvPD7MdY9OL7tjTCGCjCWi4DEZBBNdtUNdp2Q2lrF/+igWJKq6opbV199NKOIYE9HPJyqMoSp2+HgNzDmAfCJUWRqfr6+3GeaxaS5TzGyfBNsf09kYic/DQOvAxe/8wcf/hF+uR0+nQ7X/AgeCu9gpW6D764VVuQqO1EnHztNODG6BYkg2C0Y3AKFiUo1sizKQVY9LmzLr/gM/C13jewsFJaLTLG7Y8veQ35u9t3ONKdzfsJ1QWa01Aq1EWL9Xdn++CT8XbuRTamNNlNeaeKKD7exaEwElw4MbpdrZhXrCWyBPmVbGBHpzYhI74afNJu75VZkR7HqyDl6+ru2PiA2myF1Cxz8TmTpKkvAK0o0T1UUCqe6TkrfYHcevCgWV9suhCihOLVKZEJbKJMX6etCkGeVcZTJKKyl3YJh3EOKTSuwSqs4vcQMg6+E/lc2fnDfeSJIXnatCD6jJgrTErU9qLVVP2uFNrJaW+uxquclFVTqQF8iSiJCholGQLUd7FsKv98PnuGiBjpocMvLNCRJKK749RYNiItHQa9ZYvEQ3IZkgskIZ7aLv1fQYIi/rPVjKIiDnZqe/q4tzxS7OrAzOd/Ks2pfbJ8gCmAyy5zKKiHUy6nNW8NajapG4NyGjR/3pXEkvZhnfzvWbkFxid6Aq4P1DGSMJjNGs3x+S78kC36+WWRtPELBMwK8IsQWcPXPnuFg14L3xdldov7x3EFhHdxrJsRcBA4dtOtiNougsp2vn11SwZ7UAu6Z1IrsnrFSmDBs/S/kJ4HWBeLmimA4ba/IOjr7iv/XTkqUrwt3T1Ymo9nlCa6qK/50mggIywth3IMw+t5GT3ngolpNdLs/hqwjMH8paJUzRKl2tWuxgUfEOLhpFay4B1K3itepqVLsXJj04ufW4OAOPfqJco2oScJyvA3OfzVzu2sP7PxAZI5P/C6C2uiLxNhBgxvfVaksFTX6J/4QwXB5gXi8R98OD4rn9A9kTv+WK4TcNj6K60aGWXFG7Y8tKFaArOIKLv7fZl65rC9XDWu7W9/P+9LIL63k5rGRCs7ORlfDZJb5eHMSAe4ObH5kYrtdV1dhxMVKmTaDyUzvp1Zx16Ro7psSC2d2wg8LxQ17yCJRy5efDGl7RD1hbVwDRXDs6AEaB7BzErV8jl7isdNr4PRfInCLmQpJ6+Hoz6BxhIueh2G3tK9iwrmD8PsD4vuY+2Hsg2DXPjtAfx3NQpbh4r49mj/YUC6yZlv/B8VpEDAALvtYLCgu3DKWzaDq3CL9BaWVXP3xTib38mNqH3/6Brn/M5vugoeK7KWxQrxHck7AxjdEiUIDmWO90YSdSoVKJYmF6vqXIGoy9J6t6LQc7NT4uGhbp1XsHwc3r234OVkGs1EE/nWCZQOYTWDvcl5CLmkjnFwpAuIR/xKfC5aWAjl7C63u0ffA3i/g6C+w6XVRc+zoCQOuETrNPtGiQe/kShEIJ60XfxsHD1Gy0WsmnF4Nx34Tv1MXes327IYuvLagWAGyiqs1ii3rlF13IpvD6UW2oPgfzppjmaTmlfH+NYPQqFVUGs1oraAdXBtZlimpMCpu8VyNnVqFh5OW7EKdqCVc84xowLl5LfSIrz0RkTnJTxZZy4Jk8XNBChSeFXWuhnKoKBaZWBAZoMlPw/DbRWbLbIK03cJta+XDkLAG5r5Xt2bRGlQUi4Bi1xLR7BQzVdwkj/4Ms94S2SUrszclnwgfZ3o2ZdqhLxFSW9veFYuR0JEw538iEGrohixJIHXugBggr1SPm4OG9zck8O76BALcHbgozp9bxkYS4uXU/ADdBTsHuPLr8//OOiq2+Xd+ABOfqHf41zvO8Obqk2x9bBIea54WAZulzXWN8NSsOII8FNoRlaSqkokWbPX3niW+rIG9K4y6S3yVFwj3vyM/if/v7e+CT0/IOy0Wlu4hMGihCISryzlA1Fbv/0qc30HOkAAv/n6M7BI9b181sEXH55To2Xw6h7ExvjV1/V0dW1CsANXdl34W1gOHeTux6kgmRpO5zdJuNro+JjOMjPRmWp8e7D9TwC1f7uXTG4bQL7iNW30tuqbMZYOC6G/Fa0x1OsXtJ5fAkWSIvRgu/aD+9qUkiZuCk1fzNXrGSijPB3u3uplNlRpCR4gGnV0fwep/w/sjhRtX7LTGxzObRPDtFtiyko1qZFncBP96EnRZMHQRTPq3yBYl/C3qF7+YDdf9YvUShLcWDCBXV9lwhrS8UATsO94XN9/IiTDuc6Ex2w2I9nPlu9tGkl9ayboT2aw+msn3e86yaEwEAHtS8sku0TMu1tdqOyKdEv8+Iuu74wMYcWe999zRjGKc7DV4ZO+GQ8uEhbd3lFWmMneAgrbjnRFHT1F6FDdXZN33fynUNPpcIgLhHv0aXmx4hovvBSkdGhSfyCyhrNLY4uNT8kp54PuDfHHTMMa7+lpxZu3HP+iTwXpkW+hmV02olxNGs8y5oop/VmbDRh1m9gtgZj/RuBnt50KFwcQnW5L535UtW723BY1axevz+rftZFmu+jKJbcLkjaJmLm23aHypugm/XLydLMlXiPv3nmN5JkqjBdcmygQkCYbfChFj4aeb4Zv5MPQWmPpC/aDXZKBgyWw8s7aLX8k1EKlHX5jx+vkbVkPkJsCfD0LSBlF+cNU3op6wmujJcOd2+GAM/PkI3LFNzNtKSJJUP2NTWQqb/k/UiuqLhYrE2Ifa1hjUBfBy1jJvcDDzBgdTYTDV1LB/s+sMP+9LR6tWMTram6l9ejC5t1+dZIYsy6w6ksmrq04ws28Aj0zvZdW5ms2yKFuwNuMegeMrYOeHMOHROk8dzSiiX4CTeB27h4hyHyuRXVLB6Swdo6K8u39pi6s/jHtYfDWHR1VdbkEKBA2y6rSaorC8slXJvWphgKxupFVsC4oVIKu4ArVKwtvZsptddSCcmldmC4r/oWw+ncOwCC/sNeJG7upgx4KhIXyxLYXHLu5ltWbMagPKmhuV2Qy7P4LMw6KMIT9ZBFeyWQS/ZtP5n+UGZNacfCCsStO2vBD0xawPWMQjGRPYHTfHKr9Do/j1hpv/hr+fhx3vie3Nyz8W5gLVrH4Kz6ztvGO8BAMabg+WcEpeDUsmiIacqAtquw3lsPlNUY+rcYQZ/wdDbmq47lbrDNNfg2+ugJ2Lm2x4soR7vt1PuLcTD0ztWfeJVY+J2uE+l4qAp3a5Sjentk7z65f3Y8GQEFYfy2L1sUzW/3yYGD8X1jwwHoB9Zwr439rTbDyVg6uDhsPpRS3etZNlmdXHshgf23Jt6D8Pn+PeZfuJC3RnRKQXY6J9GBPtY51gMaAf9JwpXv8j7qjR160wmDidreMRj12QfUxYOWutd+9Zvj+Dl/48zsFnprZY9usfgWetoLgDKSg1ENtU6dUF+FWVjHYnAw9bUKwAF8cHEOnjYvGKP8zbGbVKItdmV/qPJDFHx/Wf7uKhqT3518TomsdvGBXOZ1uTWbo91WqZq8PpRVz6/jY+WTiECT39REPKykdE85p3NEROEDdSSS0ysCq1+FmlFvJHklrIqmldRK2cf996Mmua0znckFaEySyjbsF7Jbu4gq93nuFoRhHJuaUsHBXO9SPD2/YL2jnA9JdF5vbXO+HjyTD5GbGdfGgZ7FzMarfLWc5CnpoVh1OsL+QlYl52DaqvLhPlECEjREOPLgvWvwyFqdBvAVz0gsgKNUXsVJGh3fAa9L1ClGgoyKZTOfx2MINHL3x9nN0N+76EUXfD1BcVvWZXQ6NWMTzSm+GR3vx7Zm9OZJZQUCoUDCqNZq77eCeSJPH0rDiuGhaKg52qxQHqkfRiblu6l3mDg/m/K1q247L2eBYOGjVatcSHG5P4clsq3982kr7BVlIsGf8wLPlD1LqOfwSAk5kleJnzGZe2RCgnWNmxMLCqnvhcUbktKK6NvatIJBSmdug0isoNLXKzq8bBTo27ox3ZJd0nZrEFxQoQH+ROfJDlH2SB7g6cfGF6TWbiSHoR+aWVjIvtHrU6Nprm481JaNUqFlxgtRzi5cTAUE/2pBZY7dolFUZMZhnH6izXsV+FysO9hxTLHI2N8WVsTN3XsizLpBeWcyS9mCPpRRzJKGJGfADzh4ZQaTLzzrrTRPm64Gyv4enlRzmTV8YTM3qjUkkk5uj4z5pTRHg789C0no1c9QKiJ4sSht/uhtVPim7w9L0QMY5Py27CR61mfNX77bXdBrbqn+XXXt+g+vv5uuP4xMLCFa1rnpv2Mrw3HFY/BfM+afl5zWAwmXluxVHCvZ24aUz4+SfMJvjjAXANELa0NmqQJIneAefdyExmmZcu7cvIKO86ZXDnispZfyKHq4c3rSp0Ols0ff64N40JPX2Z1a/5Rc/0Pj0YGOrJdSPCKNUbRTm91oq35MCBorZ44+sQMhwix+PlrOXL4H0VQ0MAACAASURBVBWoCwxw8WtWVz4I8Dgvy9arR/u4wWUUlhPg7tD5yzU8wzo0U2w2ywwM9aBnj9bpm/u52tvKJ2zUZdOpHGL8XSze2hZvWpk/D5/js63J7E4pQK2SOPrcNOXsWm10SnJK9Py0L515g4PxcanfxXv3pGhUVvxQL6kQzRUuDhoRTB1fIdQTFNxKNZllsoorcLbX1GSJXll5giWbkgBQqyRi/FwwVZVyBHk4cuS5aThpNZjMMs+vOMrBtEIMZjNLt6byysoTmMwyPi7algfFIKSUrvwa9n0hnKlc/GHe58w8VIJdra3yASEeLN5g4qsRz3H9yDtFV77aXgj9+8e3vjbYK0JItG18FYbefL68xEK+2JZCYk4pn94wpKbsBhAKE5mHRPmHffeTTlISR62aSwbWbwL7fGsKH25KIsLHmZFRjZjPIHZ51CqJiT39cG2ho97UPufr4dvN+nzOu8Il7rvrYNFfhJTlQe4qUXNspea62lQbeGQUtk8QtSs5n/kfbuftqwa2Sn+3Q/AMFwv0DkKlkli6aHirz1t87SDculHW3xYUW4jeaOLGz3dzx/io1t2YG+HbXWd4avlRgj0dmdLbn7XHs8gp0dtqjLs5X25PwWAyc3NVp/yFTOhpXTmxkgph7+nmYCcclkpzRAe1gmQUljP29fW8dnlfFgwVmTeDyczUOH/unBhNrx6udRZ/kiTVZM7UKoln5/RBbzRjr1ETH+TO1cNCcXPU8N76RHJK9K2TBJIkGHwDxEwTskjO3lw3sm7QMzXOn5GR3ry19jRzH5qIewtdnppkzH1i+3rPp4oExRUGE++tT2BiT18m9apVwlGSBX+/IMpe+lxq8XX+qdw3JZaVRzJ5c/VJfrxjVKPH3Tkhmtn9A1uc/TybX0ZZpYkYv/Nld19sS2F3Sj7vXm3FRitHD7jmB+ES99U8KlQOaN1DUY2533rXrIWvqz0aldQ6rWIL0KjF/+2xjOImg+KdSXkcTi9iWp8eHXev9QwXTpImo1Xs1GVZ5mRWieIZ+mi/7rXgtgXFFpKUU4rJLBOrkIj1mBhfPr9xKGNjfDGYzEgSdbM/Nrol+84UcFFv/0ateUsqDBw4W0ifQHe8LGzobAidvipTbK8RH8waR5EpVhB/NwckCc4Vnc8SPTO7T4vPlySpJmiuto0+nFZEnq4Sg6mBZr+W4Hbenj2nRI+7o12NJrQkSTw1K46Z72zm7XWneWpWXNuuURs7RxGkHvoO9DphMGABDpn7WDP8ANryXPhpCRSli7rE4gxQaUQDYGffNu7EOGrVTI3zZ+mO1Cab7pztNTXBhizLvLsuAZVKqtMbUJuvdqTy2dYUDj07FYeq5sxcnZ6VRzLrKGZYBY8QuOZ75E8vxsFQyvfRrzPfis11tVGrJD66fgiRvso55TXFwBAPXO01lDchM/bl9hSe/e0oZhle/OM4/5oYxcPTrKs60iCe4cKMpDj9fOOdgqw7kc2iL/awdNGwemVsAAfOFnLXN/v435UDGBzWclm4w2lFbDqdwx3jo9pHScXK2IJiCzmVJWrJmhTLbwURPs5E+IgPDHUnd5CyoRxfLRpeE5g2RFJOKdd9sosl1w2us+2qFD17uLJwZBgu9irhrBQzxeKA7UK0GhU+Lvacq9o61RtNFi/4+ga782pwv+YPbAa90cTQl9by0NRY7qplkRwX6MaCISEk5eioNJoxyzKXL96Gs70GTyc7PJ20eDhpGRfjw6hoHwwmM/vPFOLpZEegh2PD2+L9FsDez+Dkn9BvftsnXXgWPp2Gj2wSixgXP3ALEnXOHqFCh9jHZn1sKfFB7uiNZhJydA1m2YwmM2+uOcWM+AD6BgsXvYQcHb8fOseISG8Gh3nWO2dvagHxQW51gt/eAW6YzDKns3TWa7arJqA/qRd/ydKffqFfH+s2113IxF5WNtGpotJo5kRmMYEejpwtaDgz/dWOVJ5efpQpvf14dHov1hzPYkCVVntKbinPrTjK3AFBTO3jb916b6gry2aFoDg5txSg0Uxxnk5PWkE5GlXrPBL2pubzxl8nuXJoCN4NlP51NWxBsYWczCxBo5JqAlklMZjMvPTHccbG+DC5dzPd7Ta6JEaTmdJKE+6Odk3WIoZ5n5frswajonwYFeUDqdtBlwlxl1jlOgHuDmRUbZ0+9MMhsooq+P52y8oIzGaZgrJKiz6Qi8pF+UhDHfEPT+vJjqR8tBrhLujv5oBObyQ5t5R9ZYUUllXipFUzKtqHXJ2e+R8KrWMfF3tW3z+ufmY/ZDi4h4pssQVBcdKfbxEmyxQv2oFncC9bRthKxAe54+9mT25JJTSwHj1bUM7iDYlE+jjXBLMvXBLP3tQC7vtuP3/eM7bOe1tvNHEovYiFI+sGPtWNf8fOFVktKL5qyQ7GxfrSJ9CNx1erSDfNYK0CTeKt4WRmCaezS1rUjGgJB84WMv/D7UgS2Gkafm/M7hdIcYWB28ZFiZ6GWsmttIJyTmaWcN93B3C0UzO1jz+XDAxifIyvdTKi1XroVlKgSM0rw81Bg49LwzuNhWXiM9CjlWViflWNqdkleltQbANOZemI8HE+b8Mry0K2SWP5i0Ojkvhu91k0Ksk6QbHZDCUZVTa6yef1aPUlgFylQ2uuMmao/d0sJK7GP9Zt3LA6ilVHM3n0x0P8fOfoJn3kPZy0uDvakZJXapV5lFUasVOrsDu2XDSTNeX8ZgEB7g4k5ZQiyzLbE3MZE+1j8Zi3fLmHrJIKfr97bJvHKK4KihtqGPF2sa8xU9FqVHx6w9A6z8uyjMksmgM9nbR8edMw0grKeeKXw3yzM7VO5hkQUnX9roAtb4Euu23203odPRK+Yy3DmRJkC4itSZSvMzufmNLo80k5OnGc3/mdFTcHO/67YADzP9zO08uP8taCATXPHUkvptJorrdFHeblhJNWzfFzJQr/BgK90cSO5DyGRnhRVG5Apzfi4WRHhI+yO0LN8cv+dD7ZksSM+ACrbrdvS8xFkmD7Y5Pxd2v4fuzuZMedExoucRkT48OWRyexOyWfXw9ksOJgButPZPP6vP5Mj1d+tw63IFHyZCUFisQcHTIw592tPD07jqHhdV9/hVWfga2RZANq/m+ziivqKLp0VWxBsYU8MzuOvCqtS0BIPaVsgds3W9zxLUkSPdwdyGxI7iTnFJTl1TJSMIkgt/rfZqP4WVKDg7v4QoaM/ZC2B9L3iSDYVEtfUKUR266OXlXas1LVdxUgiZu5pBH/zkuEL2aJruVxD1ulMaC7I8syH21Kws/NgWi/5m9M4d5OnMm3Tqb4kR8PcSy9kHXq3yB6itXUCq4dEUZRuYFTWTpydZUiO20hUX4ubE7ItcgevalMcXNIklTT0ONgp66RUPzraCZfbE/llnGR9ctE+s4X5h9HfoYRt7f6muYD3+Jk1nEs7FqmdYM6vs5Mc1JeidVB8QXB5ZBwL+6ZHMM76xK4c0JUTRZyb2o+AIPC6totq6qSH9bS7z2TV4YsQ6SPM7P7BzIu1pfickOLNMOVJMjDAYNJJlenr8kyWoNtCXnEB7rTw73+NQpKK5n23008PTuuyYy1SiXVaFs/OyeOCoPZevrKag24B1s1KB4d5cO6E9n8dSSzXlBcVFaJJIGrQwvv5bIMklTjgNddtIptkYyFhHg5ne9WzT4O+78CZFj/ijALsJAGNQAz9sOSieI6bcHJG4KGiLpRzwjwihRyUW7BLQ9u9SXwx4NCXip5E1zxWdOWuzbqsTM5n4NpRbx0aXyLbkyh3s4cOGsdreKSCiODNYmiUWvyM1a5BlDT4PHZ1mQARkU3LnPVUnr6u1JpNJOSV9rmTmhLguLGuG18JGuOZVFhMNcPiv16QUB/UULR2qDYbMaw7X2Om6MI7z9esfnaaJwf9pzlo81J/HnP2HoLr8TsUnxc7BtUJ7lrYjRT43rU2ZZfMCSU3gFuDdrpvnOV9azcq2tKq0v93B3tOsRAo1q6NL2w3GpBcVmlkf1nC7hpTATJuaW8uy6B28ZH1ri1JeboyC7R49yKOmF7jdr6Te+e4VYJimVZ5t7JsYR5O6E3mvjrWCZPzuxdZ8EX7uPM7H6BInsvy1CWD0VnoSit6uusaAKs/ndFMVz8Gr59rwa6j6udLSi2gLP5Zaw7kc2sfgGilmbDq8LRK3aasHPtv0Dc+Cygh7sD+88U1n1ww2si83v5JyKIldQiy1vjMqY67zZmNooXr74YTAZh9+kZYfl2q70rXLYEoibB7w/AZxfD9b+JzmYbLWLJpiS8nbVcPii4RcffNTG67SoLzaDTG7nKtEWUTvScbpVrAJTqjZzILGbVkUxCvZwI9rS8671XgLjRHT9X0uagOMLHhYen9VRUjqmmTrsx+s4XBiK5CeDT8BZugySsxb4oiU9Nd/FMbPs0Lf3TUaskTmXpSMwprVfmlFFUTlQjagoatYq4QLGlvCMpjyFhnrg72TXY/V8bWZYVN5uoDorDrdD/0hrOu9pVYK0lwJ6UAgwmmVFRPhhNZn7al8a4WJ86QTFAVCNqP43x718P4+VszwMXxSo+Z0AExcd/V3xYSZJqDGjO5Jex/ufDnMgsqVPucNmgYC4bFAyb3oBNb4LxguZEjYPIZLsHCxOk3NPw+304eIaz84nJeFtBFakjsAXFFrAjKY9nfjvKuFhfvHWnhAvYuEdg5J2QvBF+vx8WrRHBaUOYzWLlVZgK/n3AsX6Xcg93B0xm+fyHZMZ+OLVS2M7GNF7n1m70vxK8ouCry+HzGcLlq7phwEajpBWUsf5kNvdNjm2x/FJTNceWUlauZ1T5BrGgc7Be482xc8Vc8cF2rhwawrzBLVsMNEe0nwtqlcTJzBJmt3ENGuHj3Kh8liXIssz2pDzcHOyID3Kv61wWfzmseQq+mC1snx3chZW2vVutn93rP77tbcrs/XDuc3m3aGzpCvStakY7kl5U73345U3DKDeYmjz/cFoRVy7ZwVXDQgj2dOLyQcENbusn5ui4+qMdPD83nmkKq8z4uNgzxYrlGS0lyKPawMN6WsVDw71YumgYQ8K8kKt2VNNqKVAk5pSi1agI8myd4dbRjOJWZZdbjUcYlOWKnVgFS9jSC8vRVRiJ8XNhSm9/npAO89fRzPo1wGX5sPENCB4KvWedD4LdQ8QOc+2FWkURfHwRfH8d/jf/DWrrm7+0B4r9dSVJcgeWAWqgFFggy3Jl02d1bU5llWCvURHq5QTfvypuYCPvFMHttFfg55th98eiRrO6iS0/SXwVJIttElPVf5F7KNzwez0plsem9+Lxi3uff2DDa+DgAcNu44ONify0N41AD0cCPRwJ9nQk0MOBWf0CsVOrrJJtaJCQobBwOSy9FD69WATsGntRe6wvEb9nQYp4k8/6r8hk/8MJ9nTij7vHEtDAjbExSioMrDycyeBwz1ZnOJqjZ/k+3EyFlkmEtYDq33dAiAdXDFFmV8Feo+apmb0t6tjPKdFTYTApLtxfaTJzz7cH6NnDhSFhXnyxPYXbxkVxx4QooZE87RVhlqIvFjeZwjNVPxfXz9TUwmnSU7wyzoomDzbqEOnrgpNWzeH0Ii6/YDFX22SmMfoGu3PZwCC+3XUWgEm9/BoMigPcHcgu0XP8XLHiQfHlg4Przb0jcHPU8Ou/RltFsakaR626Tjbex0XL2Vr9GInZOiJ9nFtdT+3ppLWupXF1QqkgFXrEKzbstzvPsHhjIsefn46vqz23jo2sFxDPW7yN68y/MtekhxlvgH8zuuwO7nD1MvhoEuZvFvCey93E+6iZGO4APfoi+8SSo9M3WCbUmVFyyXMN8B9ZltdIkrQYmA78puD4nY6TWTqRpco8ACd+hwlPnM/29p0H+5fCykfqnmTnLOp3fXtC7HRRz2vvKupzP59VLzCuE9TWzhI7uBHgXkKkrzMZhRUcSS8ir7QSrVrF3P7CrvTJX4+QmlfKZzcMO6+OYS0CB8LC32HpJbD8zrrPqTTg7Asl5yBujlgk/IOpXqxUb6u2lHKDiUd+OsRzc/ooHhTf6bMfQ64rdtEXKTruhfhX1RBuPp3LlcNCFRv3htENOwG2lMUbEvlu9xmOPq9s6Yi9Rs11I8J4a+0ptibkcVGcP6Oq7IJlWUYacXvjNcXGyvPBckVRzc9FJSVo+16KZabyNlqDWiURF+DGkfSiOo8fzSjio01JPHBRT0K9m15QPTe3D3tSCygoq6zZxr8QJ62GCG9njp8rVmzu1ZjNcqcwV5AkiQEhHs0f2EaKyg18vDmJeYODCfMWgXewpxNnC84HxYPCPBkY2vo5eDjZcTLTOuogQF1ZNgWD4sQcHWFeTjVxwOMzetc7Jre4jLGGXyF8bPMBcTVekTB/Kaqll3B33j2QCuwF1FoSBv2bizZHceXQUJ6Y2Vu4pXYBFAuKZVl+v9Y/fYFspcbulBj1uGRs51r3BPhxi8je1r65SRJcshgOfAPuQeLF4xkh5Jcayt56R8OXc+HzmbDwN3E8om75xT+Oceu4KAZvFVni5drZfPPhdj65YShzBwTVDFFeaSK7pKLmgy/Y05Fvdp7hky3JIjNlbXrEw70HoSTzvCKGnZPYfjGb4L/xsPPDf3RQbDSZefjHQ0jA/13Rv1U3KV8Xe5y0auVl2QzlxOZtgPi5QmrPithVNSn9cfgc7yk4bqneyJH0IvoFe+CobX0zTFG5wWrbyovGRqBRS0yN8yfG35UKg4kbP9vFuFhfbmwqmNdoQeMDznXrkp/97gA71u1g22OT2mcnyAYA0+N7kFFYN0t4OK2IXw9k8ODUns2e7+pgx1eLhpNZXNFkhrJ3gBuHLwi+LUWnNzL4hTU8O6cPVym4GG0rm0/nkF5QrujCuJo1x7J4Z10Co6N9aoLiaD+XOhnetpZKeTppKSiz4gZ4TaY4RdFhk3JKGeBVCX89CSPuBPcgMgrLqTCYalxU+5XvxIssGPp66waPGAt3bGPVtj18sCOX/14znPAD/0fM7qf5j90Yntx9E/OHhjAotH55aGdE8eIYSZJGAp6yLO+44PFbgVsBQkM7/k1pEQUpmD+ZxvvGTMx5KggaBFNfql+L6R4E4x9u2ZiBA+D65SIwfnugCIoDB+JuH8DUUwcILtBD/m6Y9G/2Zhk5llGM8wU3f0etuuZDAODOCdEcOFPI23+fZu6AwJoGB6uidQbvBgJwlRqG3AQbXhFybg0d082pMJi459v9rD6WxUNTY1udtZEkiTBvZ84obOBhOrkKdWUJlXHzaI9Wiccv7tXg1rEl7EzO46bP9/Dj7SMZEt5yi9JqisoNDWoUK4GLvabOTdjBTo1Ob+TTrclcPzK8VVu4uTo9G0/lMC7GxxYQtzM3j42s91hijg57jaqmTrY5Qr2dms0o9w5w5Y/D5yipMDRp6NMaUnJL0RvNeLbSmMFa/H7wHOtOZiseFCfm6Hj2t6P0C3av4yT4f1ecbzbQG01ISG3aPQ33diLK18Ui+ccmcfQUvQMKBsUms0xKbgmL+R+c2QnHV8DCFVzxYSL9gt1ZfO1gjCYz80wrKXH0w7VXGxwOfXsyZHI4h3f+zbdn3Zk65kPWH3+UB+x+ZKTqGJV/T4BB0yF8jIiLOjGK/lUlSfIC3gFuuvA5WZaXyLI8RJblIb6+TXfedmoqiuGbK1EZy6m47HN09yXALX9DrxmWjx04AG5ZB5OfFo13Z3fjuv8DhqtOYDLoRVA5/A4Sc3RE+rm06Kb41Kw4ZGRe+uO45fOzlME3gsoOdi3p6Jm0Ozq9kRs/283qY1k8OzuuvqFDCwnzclI8U2zYv4ws2YOf8sIVHbcxbhsfVWeHQwmqrUuPt3Frs7jc0GonJ0tYNCaCs/nlrDmW2eRxsiyahLaczmXymxsY8uJa8ksrmWllNzAbDWM2y5TWsmNPzCkl0tdF0bKEkVE+3DAqHL1ROaWZpBo5tvY16miMQA9Hckr06I1NNyi2Bp3eyG1L96LVqFh87eCaXakLWXc8m95Pr+JUVus/K64bGc6Ku8dYJyAGsYvsGSZqihUiraCMa/mDyKKdIktcUQifzWBGUBm7U/KRZRldxgnGqQ9zOuQKULftc9DHxZ4Jsb78uj+dd9cn8rX9fIqv+JFD5kj809fCL7fCW3HCY6ETo2SjnRb4AXhclmXr+BR2NCYj/HgT5J2Ga3/CIXICim82e0fB2Adr/inJMnNfXMvUiB68MqsvAAnZOsZEt2xhEeLlxF0To/lieyp5ug62YXT1Fx33+7+GiU+Kjvp/CLd+uYddKfm8taA/lw5se7NLmI8T605kYzLLDWYYjSYz5QZTyzNM5QXYJ//NCtMU/B27rpJBgLsDrg4aTrSxFrOo3EC4j7JNdk1xUVwPQrwc+WRLMtPjhVueLMukFZSzIymPXcn57EzO59HpvZjZLwAvZy2hXk5cMSSEUVHe9Au2Xk2mjYYxm2WGvfw3c/oH8vRsUXOZmKOrUaZQisFhnnWynEqQnCOC4rBmstTtRYCHuHNmFembzZy3lDdWnSA5t5Sli4bVy9wnZOt4evkRHprWk8QcHSazTHArlSesyaG0QlSSRHyQu1CgyFUucPTTneRJ+++piLgYh2kvQ/+r4Mu53J92H24Vo8lfexjXrF0YJQ0MXmjRta4dEcYv+9P57WAG90+JxaNPDE9oZab19uXlURKc2QE+bUsItRdKLncWAYOAJyVJ2iBJ0gIFx+54zGb46wlIWAMz3+SbnEje+fu09a8rSfi7OdTUQ5VUGMgq1hPl1/LO3VvGRbLuwfGdQ75p+K1QWQIHv+3omViVXJ2ep349QkWVVNP1I8NYumiYRQExwC1jI9n++CRq4uH8JLHISN9Hpb6CMa+t5511CS0brCQL1jyNZDaw3DQal5Y6GXVCJEmidw+3NjfB3DslhmuGhzV/oEKoVRI3jIpgd0oBh9IKyS+tZNSr6xj7+noe/vEQa45n0auHK57OYnETF+jGZzcO4/bxUbaAuINQqSRCvRxrmu1MZhknrcYq1rZms8y2xNyanQJLSckrJcjDscXyj9YmsJaBh1Lcf1Es7109qEFtcDu1xLbEPBKydSRk6wjycGxWMaQhjmYUMfudLew/o5yJUqXRzM1f7OG+7w6IBwIGiKD49JomzyuvNGE2N/L6kGXRnJtzEsffbkXl7IPD5e+LTHRAP7jhDzSObtypXo731ufQJqxE028+g+Kar41viom9/Hj7qoFsfHgCN4wOB0Rv09lCvfBsGH5bp7ekV7LRbjGwWKnxOg16nWiW2/Uh5CXAiH/B4BtYsWQHFUYTd0+2/qqnV4ArpqoXf0mFkfGxvvQLavmNsdqJx2gyk6urVLyes1UEDYbgYaLhbugt3U6eTZZlftmfzgu/H0OnN3LpoCAGhXrWZAMtxad6YVOUJkTW938lDFoAlWTHV6pQCva4IBcHI9k5g9ZJKJ5onUTTo9ZZfE/dBkd+BJOBzIjLOHw8ArcuHBSDeJ/8vC+dSqO51fWCM/oq8/dpDfOHBAsZOE8nPJzsmNDTl7gAN4ZFeBPjp+yWvA1lGBTqycdbklnw4XauGBLCL3eOskqgueJQBvcuO8CyW0cwItJy18eRkd4tspJvLwKrMsVKaBUfP1dMpK8zHk5apsc3LGMX6OGISoK0/LKqkpe2ycFJSBxOLyKzSDlZtj8PnyO7RE92iZ70wnKCRt0lPA9+uQ1u3yI0zC/AaKjk/lff4aY4GOZjBF0m6LJBl1X1lQ1GMUcZiaNTviTeqVavhX8cdvfuZegLq5kW48yzFwWhcg9EqVdy7d6m/ywY0HLr6E5A15lpe1OQArs+gn1fCimkoCHCQa7PZZjNMiezSpga598uU/nP/AE1Pwd6OPLFTcPaNM7VH+8EGb6/faRSU2sbw2+DnxYJg5OoiR07FwV5fdUJdiXnsye1gEGhHrx2eb869q5tpiRTaNnmnsZYcJa0lNOEFu9FhSzqzAddz+rNW0k6uJmp3jmoC/KoyErEkQqoLANDGVSWUscW3M4JBi2EEXewJ90Rju/Hxb5zNOG0lauHhzJ/SEirA2KTWebA2QLCvZ3bdTfF1cGuTgPeK5f1a7dr22gb910Ui4eTHT/uTePp5Ue4uJEgzFKmxvXA3dGOpdtTFQmK5w/tXE6joV5O7Hpi8vlFfhs5k1fGlUt2MK2PP6/Pa9y5x06tIsDdkTP5ZSTm6JjfRo306r6DgjJDm85viOPnivFx0VKqN3Eqq4Sgnn4w7zNYMgF+ulk4xao1IvubdQQOLkM6+D0fmLPhiBijUuuJ7OKHvUcAhI4UKlcu/uDiz0MbjZw54sMPY+peV5Ik3r12CKFeTvyenMcDb65l/YMTFHc8VFo+1NrYguLayDKkbIGdH8DJP4X5RNxcGH6HMKio4veDGeSXVjI6ugkLV6tNse2GHJN7+fHKyhOcyCyuaUzqEHrNFB22h3/o8kHxruR8CsoqmRrnT2peGcUVBp6dHcd1rVQVqIfZDGufEfrX+Uk1D6udfSkpceVAj5kMuvp58AjFbJZ5MTmXHiH9uer6IUx7cQ03RIfz5MxaWpOyDIby8wGyk1eNY1Jvs45/z+zdsTsICtDW13R+aSWXL97O83P7cP3IcGUnZaNb4WKv4a5JMfxrYjSpeWU421vnFuqoVTN/SDCfbU0hq7iiRt+7LeiNJkoqjHg7azuNYolGrcLPgt8JRPnAbV/tRZblFkmsBXs6kpxbyr2TY9ps9OPpJPR5CsuVk2V7fEZv7p0Sg1olYa+pytX6xsKs/4hs8Z8PiiTGyT9Fsk5lR1HQRB4vjOOmBfMY1DuWCW9uIStTz7Vhodw7JRavWpbLG39bw5TeDQem1QuuwjIDsoxVZCmTcnT8efgc144Iw8Op81tBd6+966YwlFdtN38Niesg+wQUnoWidCjOENvQH4yFL2aJreUx98N9h2Hep3UCYoPJzH9Wn6RXD1dmWdLCLQAAIABJREFUttOW65bTucx9dwvnisq546t9LPx0V5vGmT8kBHuNiq92dHAfpJ2jMPE49pvIZHZhPt+WzAu/H0OSJN67ZhCr7x/PDaMjLAuIAfZ8AtveFtrWU1+Em9fBk1lIDydwr/tbLHG9GzyEnNGWhFzO5JdxzfBQ3B3tGBvjy5+HM+vWI0qSKKFw9hHdzbUsRKN8Xbh5bGSH278qQaXRzP3fHeCjTUnNH1xFUbnI+nSH399G+yBJkuIZtQu5dkQYJlnmm51nLBrncFoRQ15cy8ZTOQrNTBmW7TrDZ1uT23SuLMs89vMhTmQW8/ZVA+ts1zfGoDBPwryduW18VIN1xy3BUavGXqOiUKFMcUGpCK6dtJrzAXE1/a+EAdfC3s9h9yfg0xNmvQUPnmTDwLf4yzwMn8AI7LT2rLh7DFcPC+WrnWcY/8Z6PtqUhN5oorCsklxdZaPZ2gqDiaXbU1h3QthKWEOWMjWvjP9bfYrEHJ3iY1uDf06muOQcrHux6WP84mD228Lq1q7hztRSvZG+wR7M7R/YbjV/BrOZg2lFZBSWcyqrpFE3pObwdNYyu38gv+xL59HpvRTTwGwT/RaIhciplUKRogsiyzJ7UwsYHmH59mYdCs/C2mchahJc+1O9xoRwb2f2nSlgT0o+Q8K9+GbnGbycz9fT/WtiNBUGE7Lcsp6GrOIKSvXGGhH3roxWoyKvtJLFGxO5enhoizJ5tqDYRmckzNuZ8bG+bDyVw/0XxdY8bjbLZBSVk5hTysBQD9wc7Fh+IJ131yVw1bBQrhkRWifAqpZjC29B4NiebDyVw74zBVw3IqyOxFlxhYHvd5/lupFh9QPFKj7flsLyAxk8PK0nE3r6teh6j07vxbmicjIKywlwd2hz1nxcrC89LMxyg2gynPjGBl6+rC/zBgdzMrOE+787wPNz+5zXWp/1FvRfIHpxtOf/fukFosm/WmXD28WeFy6J5/qRYbz053Fe+vM4QyO8anqRGquh1qgkXll5grJKE24OGsuTOQ0Q4iXmmFZQzuD262VuM/+coNgrEp7MFMFx8Tnx3VAOsll8eUcLYelm3igeTlreuWpgO01aUP0GTCsoJzW/jIv7tr2O7boRYfy4N42VhzM7ts4sbAy4BcGh77tsUJxRVEFWsV5Z+SRZht/vF99n/bfB1+PNYyO4d9kB5n2wnQ0PTUCrUXHVsJCaG0hr5/PhxiS+33OWI89NU+RX6GjunxLDpe9v44vtKdw5oflt1WJbUGyjk/LGvP54VlkLv78hgYRsHUk5pZRXqdosXTSMsTG+BHo44uqg4fnfj/HZtmQemtqT2f1E4iY5txSNSupUEmQAlw0KZuWRTFYeyWR2//PNZGazzE/70rFTq1g4KrzBc8dE+7BoTAR3ttKpdfGGRH7Zn86hZ6a2ed4fXT+kzefW5svtKZhkmZFVtu893B04mVXCxlM554NijRYixtU7N8zHmcsGBtVr8ozxd+XzG4dxLKOYuEA3Np7KwdtZ22imWKNWMSjUky0JuXg6W6e0IchDSO6lFSinNGJN/jlBMYjsr1dkjYVya1lzLItQLyd69lCgeaoVVNeT7UzOx2SWLeoi7h/iwQ+3j2RwR1suqlTQdx5sfw9Kc+tZ2XYF9qYKWR5F7SsP/yBk/6a/KsocGmBUlA8bH57AplO5hPs48/ZVA+tJNyVkl7Di4DnumxLTbEZEpzd0qe7g5hgY6smEnr4s2ZTE9SPDcWkmW1xdH2gLim10NnxdRSOawWRmT0oB0X4uDI8QShJRvs70qdJIHhruxU93jGLz6VxeXXmCe5cdYMXBc3y8cAgpuaWEejtZz3CijUzu5UeEjzMfb05iVr+Ams8pd0c7HOxUfLQ5iauHh9Yx4dDpjThr1cT4u/LUrLjGhm6QU1klfLldlA52dG11WaWRZbvOMq2Pf022193RjoEhHmw8ldOsbfic/oHM6d+4gU9coOivGBLmyZwBgYR6Na4FPTTciy0JuU2OZwmOWjU+LlrO5neNUsnO9S7pxGQUlvPIjwd5+c/2d4bzdLJDq1axNSEXgGhfy4LyoeFeqFQSqQo7o7WafguEnNjRXzp2Hm3kaHoRjnZqegUotEgqzYWVjwqlk2G3Nnmok1ZTR37owg/5Q2lF/O/v0+w/W9jsZUsqjM0Gjl2NeyfHUFhmYNmu5usxh0V4885VAwlw71yZNBs2qokPcmfrY5P44qZhPD07jquHhzI80rvO+1aSJMbF+vL73WP435UDmDdYaKJnFVc0GRR1FCqVxE1jIjiYVsTuFJFgeG99Ah9uSuJfE6JJKyhnxcGMmuMrDCau/mgHz6041qbruSlULvjSH8eY/+F2i8b4eV86ReUGbhwdUefxcbG+HE4vIk+nb/J8g6llbofO9hqemd2nyVLPoREiqTMw1Hr658GeTopqUlsTW1DcAvak5DPn3S0YTTKPXdyr3a9f/WEXH+TOlUND2qyxWJvdKflMenMj761PUEwgvtX49wH/vnDou465voU8dnEv/n5wfKN2oq1m5aOgL4G574LKMsXIKXH+aNUq/jx0rtljdXpjt8oUg8gWPzs7rkX6w0EejszuH4ijtnMYG9iwYQkqlcTcAUE1i+aZ/QIZ0wFKSS1h3qBgxsb4IMsyeTo9765L4MS5Yib18qOnvyuLNyRiNsvIsszTy49wKK2IUVFt6+Hwc7XH1UHDs7Nbl2G+kLJKEwnZljWNfb3zDH2D3BlyQanb+FhfZFk0TzeGLMv0f241b61RxvVuYIgnrg4aUnKtl8n9/MahfH5j26Rk25vudSe0Ast2neGp5UcI9nRi2a2DifZr39KJaj5eqEwdUzX9gz2Y1S+AN/46SWZRBc/O6WOVIvtm6Tcf1jwF618GzYWalRfMp96WVyufD+gHkRPaNM2GkCSJQA+Fsoun/hJmGhMeB7/eFg/n5mDHuFgf/jx8jidm9G4yU1BcYeyWpQM3XJCFaYzj54opLjcwXAE9WBs2OhuLxrTsfdAROGrVLF00HIBXV56gwmjirkkxqFQSd0yI4r7vDrAtMY+UvFK+35PG3ZOimdqnbT01KpXE4Wct75vwdNJSWFaJ2Sy3udn+ixuHkqPT19vh6xvkzmUDg2rKZhoir7SSskoTnk7KfGY7atXcPyXWqmWhXUGKrRpbUNwEsiyzPSmPkVE+vHPlQNwVehG2leziCnxc7BVRvdBqVLw1fwB+rvZ8tDmZGH+XjtFo7XsFbPo/2Pia9a+lcYQHjgmNXgs5nFbE0h0p3Dcl1vLAuKJYNNf59oYxD1g8t2pm9A1g7fFs9p8tbLL57t7J0Wi6mbNgNXtT84XD4Nz4RusIP96czPbEXLY9PrmdZ2fDhg0QWrYfbExk7oDAmp6ZWf0C8HdzQKuReG7FUSb29OW+KbHNjGR9PJzs+H/27js8qip94Pj3THrvJIEECGDovRepgjS7InbFjr277sqyYl1c1/ZDUbEAKrCiqCgKKILSexHpJRAI6b1O5vz+uJNh0kibMAl5P8+Th+TOveeeuRMy75z7nvdYtJF2VtuYoJm/Z4V1mk0mxRvXG4t1/d+qQyzbc5qOEf68fHVX2x3JeOuEtRZBjkuJmVLPH5z2xGfw+cY4nhgTW+cFW+qbBMUVSMspJLvATHSwN69d0w1Xk3L6JIUP1xzhpR//4s4hMTWeYFAZk0nx9wmdWLTlJAfPOKmGoH8kPHPUtlQxYFReKKWC9I6q9in7ePIB+HAEbP0ELn6itr21WXs4mUVbTvLsuLqP6rJyulEr+665xmxjB7mkUzjBPu7EpeacMyge2eH8rMzoDIeTcpi/IY4xnSIYGhtW4T4ZeUX1Up9TCFE9L/5gzNV5aOTZajGuLiYGtg3ht/2JdIjw583rezrnbmYZJQt4pOUW1jgoPpSYzT+W7ObFK7tUedc5zNcDD1cX/rf1JJP7tbT9DS/JzS1ZKrsxSM4u4MtNcVzTq4UExY3N/oQs7pq7GX9PN75/cEi9rGtfGyX/EerjF+r+4W2duxSjyaXOObRVatEL2oyAjR/AwIfqHHxuPZ5GTKhPqZWDauX4OmOhjgFTIcqxKTL+nm5sem5UlR/oNh1NJTrY64KcaHZFj+b8Z/l+Zq85XGlQnJlXdEGmjwjRWPz3+h4cTsquMFAc3r4ZQy8KO2/rAlQlJsyH8V0jahWgf7ruKNvi0quVTjCpbzQXx4Yy8JVf2ROfcTYoto4URwU2vMmTlYmyjmqfSMs9W26ugbow75lWQWvNT3tOsz0urdT25X8mcPWstRQUWXjxyi4N5j8hgL91IlR9fDq8b1hbRne6cEcLbQY+CNkJ8OfXdWpGa832uLS6l2IryofvrCvTjfxH3dqqRElAXGiueLayudjCpNnrWbT5ZL2c39k8XF24Y3AMaw+lsCc+o8J9MiQoFsKpArzczvn3tCG9F/dqGcSsm3oTXcOKHhm5RSzeGs8V3ZtXe3Arwt+TUF93dtv97erc3J87h8Tg79V4xjRLamSfTG34FSiaXFCcU2Dm4QU7uG/+Nq6atY5lu0+jteadXw5yz7yttGvmy/cPDaGns+v4lnH/8Ha8cEVnJnZzfC3B3EJzo6khWCftRkFYB1j/bgXpF9UXl5pLcnYhvVrVsYTN6tcg5RBc9lap1YocqdiimfD277y6bF+Fj+cUGIsAXGjVJ+zd2L8lvh6uzK5k6WcJioUQ9W3B5jjyiorLlWE7F6UUN/VvRY/os+81g9qF8vzETk6vtVwTnm4uhPl5NIoFPC7cd8IKHDiTxf3zt3I0OYcnRsfi5e7CiA7NKDBbWPHXGa7q2YJXru7aYFIm7Hm5u9TbRLiZP+/nf1tOXjArmlVKKSNN4fuH4djvFa4UVB3J2QXEhPoYt7NObjWC7LxUKMyB4iIjyG3e49yNnN4Fa9+CHjcZyznXExeTIjLAix93n+YfE8pXocjMN1Zz872Ag2J/TzceHNkOl0reRN69safkFAshqiUjr4hhM1fx6KiLql3hxlxs4bN1xxjYJsS2sEZ12S/xDZCQkU+wjzvuro1rTLN1iLdtJcaG7MJ9J6zAtzuMgtnz7+zPoDJ1Gz+/qz++Hq6N6tOXo4T6epBdYCa/qLhBfiBwqG6T4JcXjJX0ahkU924VzKrHBsOamUblDK9AY5VEd184udlo+5oPK2+g2AzfPQjeITDmxVo+keqb2C2SlX+dYfuJNHq3Kp3PlV1gTHD0v4CDYjBShCrT0HPchBANh6+HKxl5RaTmFFb7mGKtufPiNnSoZdmzzPwiFODn6caY/67mih4tmHFll1q15SyL7h3YKOKrxvVRo44euySWHx+5uFxADMYvW2N4wepDmDW/KSnr3KvoXBDcvKDf3XDgJ9jySa2a0En7Yc5oI/2h63Xw0Da4ayXcugS63wB7v4W8tMobWPtfOL0Txs90SHm4qozq2Ax3VxNLK1jIIyvfCIp9PS78kdKiYgvfbD9Jmt2bWW6hmSXb45tG+pAQos5cTIoALzfScouqfYyHqwt3DolhcC0WUUnMzKfb9OV8vS2erPwiMvPNtAhqfJOiG0t81aSCYlcXE838Gk8Zk/OlpFB4UhVLS14whjwG7UYbtYF3fFH94ywWCta+R8H/DaEw6Qhc9xlcPdsYKS7R61YoLoBd/6u4jUMr4deXoMs10OmKuj2PavLzdGNYbBjLdidgsZTOpW7XzJfZt/Smo6OWqm7AjiXn8NjCnczbcNy27VR6Po8u3MG2uHN8iBFCCDtB3u6k5VZvpPiv05ks3BxHgbl2qQNhfh6E+BiT7U6l5wPGKpyNTWJWPluOpVY66buhaFJBsahYyUzY5KYwUgzGynnXz4M2w+DbB2D3V1Ufk3kK5l+Nx4pnWV/ckS0TfoTOV5bfL7IbRHaHbXPLT+ZLPQJf3QnNOsHl71SwAl/9uXNIDH8b3wFLmT4F+7hzaecIQhp47UhHuCjcjxHtw/hs3THyrbltGXnGaI/kFAshqivQ2430ao4Uf7DmCDOW/lXrYFApRdeoAPbEZxCfbtzRaowjxc38POnTOrjB50I37N6J86JliDfTJnaq12UeGxw3L5j8JbQcCF/fDZ9dBhveh/S48vvu/gpmDYQTG1nV7lnuKHqazrHtK2+7161wZjec3nF2W2EOLLjZ+H7y5/VWbaIyA9qEcEWPFuVqFh9PyWHV/sQG/+ndUe4Z2paUnEIWbzNK0GVag2KpPiGEqK7xXSIZGlt1KkRiZj5Ld53i2t5R+HnW/m9M1xYBHEzM5nBiDgBRjXCkuLGQoFgQ4OXGlCExtAo5v4Ga07l7w42LjHSKrDPw0zPwZld4bwisehniNhoju4vvhJB2cN8fzC0axUXN/M69klGXa40lpbfNNX7OSYaFt0DSX3DtxxBcv0tqViYhI59P1x4tlULx4+4E7vhkM2ZL0wiKB7QJpntUAB+uOUKxRdtGiiUoFkJU191D23DP0Mon75ZYvC2eomLNbYNa1+l8XVoEUGzReHu48Oy4Dg1+VbjG7MKeci6q7Wiy8Qk0JrSJBcYevjBqmvGVchj2/QD7f4TV/zYm0plcYcQ/YMhjWJQL208c5tJOEedu0yvQyBfe/RW0vhiWPQ35GTDxv0atZCfZeDSF6d/vpXOLAPpaKy5sj0vD082E14VedcRKKcU9Q9vy1i8HOJOZL0GxEKJWCszFeLgafze3Hk8lI6+IkR3OLoKlteab7Sfp3Sqozu+rfVoFMePKLozuGE4zf5kXVZ8kKBYA3D13C+3CfHn/lt7O7orzhLSFwQ8bX9lJcOQ3aNYRIozSNwWFxVzbK4pB7UKqbqvXrbBrAXx1B4R3gVu/hfDO9dv/KozqGI67q4kfdp2mb+tgftx9muV7z/DkmNhGMzPYEcZ1iWBclwhMJsXl3ZvTPTqQoGosuyqEEADvrz7Mq8v2sW/GWDxcTVzz3noAdkwbbVvCOTPfjKebC1f2bFHn84X4enDLgFa2VTklMK4/Dk2fUEqFK6V+d2Sb4vwI9XUnualUn6gO3zDodp0tIAZjAZV/TOxUajSgUq0GGeXaLn4C7v7V6QExGPU1h8eGsWzPaZKzC3h+yR66tgg4Zw3fC5HJpDCZFNkFZnKLiukRHYhLA1pGVgjRsJWsAJqeW0RGXhGhvkYgPHf92co2AV5ufPfgEG7q19Ih5zyVnsfEd/7g9eX7HdKeqJjDgmKlVBDwGdDE7r9fGEJ9PSQorsKJ1NzqT0hTCq75yEjLcG04+V8TukVyJrOAlXvPEBHgyczrupWbfNcUWCyay975g2H/XsX3O085uztCiEak5M5SWm4hgd7ubPnHaEa0D+NTa2WbYosmy7paaNlVRGvrs3XHAAj2aTjvJxciR74bFgPXA5kObFOcJ2F+Hk1j8Y46uPOzzdw/f6uzu1EnozqG4+3uQn5RMUsfGkKHiJotOXqhMJkUV/RojtmieWzhjqoPEEIIq0DrROvTGXkkZhq1g+8b1pYALzdOpOay4UgKfV5cydbjjqt/Hm5NmSgqbhqTop3FYUGx1jpTa51R2eNKqXuUUluUUluSkpIcdVrhIKG+HuQUFpNX2PDXJneGjLwiDiZm0z06sOqdGzBfD1c2//0Sbh8c06TyiCty68DWAJjLLGgihBDnEuhljBTPW3+cga/+yqHEbPrFBLPy8WFcFO7HN9vjcXcx0bm54wYdrusTxeXdm3PP0DYOa1OUd94m2mmtPwA+AOjTp4+8CzUwl3YOp02oD6amdye9WnacSEdr6NUyyNldqTMfD5lfC8bCJW9N7oGvXA8hRA00D/Tk3qFtmL/hOK1DvGkb5oNSChcFqTmFfLX1JJP6ROHpwKo+fp5uvH1DT4e1Jyom7wYCgHbN/GjXrAkt3lFD246nYVLQPTrA2V0RDnRFj7rPDBdCNC2B3u5c3zea2WuOcF2f6FJ33XrNWAHgkKoT4vyTcUEBQH5RMX8cTOZUel6NjzU3gRynbXFpxIb71WlVIiGEEBeGT9YeA+DqMsHvR7f2YVyXCAbEVKN0p2hwHB4Ua62HO7pNUf/Sc4u4ec5Gfttfs3zvn/9MoMcLK7jrs82cTMutp9453yOjLuLZcR2c3Q0hhBANwLwNx/F0M5WrGXxJp3Deu7m3w6pOiPNL0icEACHWOos1qUCxLS6N++ZvpV2YL2sPpTD6jTU8dWl7pgxxzjLG9amPdQU4IYQQYs1TI2QlzAuQBMUCADcXE4HebjWqVdwzOpAXrujCdb2jSM4uYPp3f5LkgFrHFovGbNGYFLi6mLBYNJn5RRRbNMVaY7FAsdb4ebri7+lGgbmYuJRcirWm2HL28aggL0J9PcjKL2LXyQxaBnsTHexd4/7sPJFOak4hQ2PDZJEHIYQQtAyp+XuJaPgkKBY2YdVYwENrzVu/HGRit0jaNfPjlgGtAIgK8ubDW/tQUt2q2KKrHUDmFJjp+cIKW1Bb4uFRF/H46FiSswvo9/Iv5Y57bnwH7hnalvi0PEb/d025x1+6qgs39W/FseRcbvpoI24uiqcv7cCdQ2JqdGtr3obj/Lovka3/uKTaxwghhBCicZGgWNiE+la9gMecP47y5sqDaA2PjS5draKkJM3aQ8k8/+0eFtw9oFprtLu5mJgyJAYXE7gohYvJhIsJ+lpTFnw9XZk2sRMu1uV5jX2w1Qxu5u/JOzf0NB5XCheT8Xh768IUMWE+fHn3AD5Ze5SXfvyLNQeT+M913au9fvy2uDR6tQxq8nV9hRBCiAuZ0vr8lwzu06eP3rJly3k/rzi3XSfTcTEpOjevuOzYyr1nuHveFi7tFMGsm3pVOtp6OCmb8W/9zpB2oXx0W59Kg8n/LN+Pp5sLU4e3PS8Bp9aaLzed4IWlf9I2zJfvHxxS5YhxWk4hPWes4Omx7Zk6vF2991EIIYQQjqWU2qq17lPVfjJSLGy6RVW+WtveU5k8vGA7XZoH8N/re5wzmGwb5sszYzvwwtK9zPrtMFf1bEFkgGepwHftoWTeXXWIG/q1PG8jsEopbuzfkj6tg/B2d6lWCsX2E8YynRfCoh1CCCGEqJzUKRY2x1Ny+N+WExSYyy/1POu3QwR4ufHRbX3wcq96lZ7bB7VmSLtQZv68n0veWG3LNf5wzRH+9vVuHlu4gzahPjw/oZOjn0aVYsP9iAryxmLRfPT7EbILzJXuu+NEBi4mRfdzfGAQQgghROMnI8XCZuORVJ5evIsBbULKVWl4/bruJGTkE17NPFyTSfHJHX3ZHZ/BmYx826S7v05nsuZgEh6uLrx9Q89qBdj15c9TmbyybB+/7U/imbEd6BDph5tL6c+Jj4y6iKt6tnBqP4UQQghR/yQoFjZhfh4AJGYVEB1sjKS+t/owN/dvRYC3G61DfWrUnpuLqVzawRvX93BYf+uqa1QAr1zdlWcW7+Kyd//Ay82FHtGB/PvabrYPBS4mRUwNn7cQQgghGh8JioVNqK8RFJeUZXtjxQHeXXWIYB93bujX0pldqzeT+kRz8UWhbDmWxra4NIK93YkK8gLg4JksPll3jPuHta1VfWMhhBBCNB4SFAubkpHi5OwCFm89yburDjG5bzST+0Y7uWf1KzLAi8u6e3FZ9+a2bUeSslm66zRfbIzj/mFtndg7IYQQQpwPEhQLm5Klnn/cfZpNR1MZ1DaEGVd2aXL1eQvMxdz80UZOZeTTzM/DNnIshBBCiAuXVJ8QNm4uJpY+NIRjyblEB3nz3k29y008awo8XF2YfnlnTAp6t5JFO4QQQoimQBbvEOUkZuaTV1RMq5CmPcFs/eEUWgR6yRr3QgghRCMmi3eIWqvu8scXuoFtQ5zdBSGEEEKcJ03v3rgQQgghhBBlSFAshBBCCCGaPAmKhRBCCCFEkydBsRBCCCGEaPIkKBZCCCGEEE2eBMVCCCGEEKLJc0qdYqVUEnD8PJ0uFEg+T+cS5yavRf2Ta9xwyGvhXHL9Gw55LZxHrr2hldY6rKqdnBIUn09KqS3VKdgs6p+8FvVPrnHDIa+Fc8n1bzjktXAeufY1I+kTQgghhBCiyZOgWAghhBBCNHlNISj+wNkdEDbyWtQ/ucYNh7wWziXXv+GQ18J55NrXwAWfUyyEEEIIIURVmsJIsRBCCCGEEOckQbEQQgghhGjyJCgWQgghhBBNngTFQgghhBCiyZOgWAghhBBCNHkSFAshhBBCiCZPgmIhhBBCCNHkSVAshBBCCCGaPAmKhRBCCCFEkydBsRBCiEZFKaXO53FCiKZBgmIhhKgDpVSoUmqb3c8tlVJLHRmAKaX+rpQa4qj2qnnOzhVsG6aU+qKG7QxWSgVXsU+EUmpNda6ZUsoL+F4p5VHDfnhYj/OqyXFCiKZDgmIhhMMppf5SSiVYv04qpT5SSj1l9/jXSqkp5zh+ulIq266NBKXU9UqpT5VSGUqpJKXUEaXUzXbHvKCUSlRK/VIShCmlXJRSH1r3/19JIKWU2lam7WJrMOtt7fuOc3ztVEr9za67+UCm3c9TgS7AKqXUb0qp6+z6OFAplWN33v9Ytx9TSqUopc4opd5WSrmXuSSXAgWVXCtXpZSr9fsgpdRKpVShUmqvUqq7dbuPUmqJ9frMsjtWK6WSra/RtJKgVCkVCaxWSr1a0rZVIVBUQR9c7PqglFKedg//C2hfZv8BSqm11uvzG7AAGAysK9mmlFqnlJpQwVOeCXyktS6wXrfWFV2XsrTWBcAc4PXq7C+EaHokKBZCOJRSyhvQWusI61cU8AZwv1LKpJSKAvoDn1fR1Lt2bURorRdatz+jtQ4DrgNmWUdqJwBXAe2Aj4AXrfveB7QGWgDbgEcwOterpF1gOLBNax2ntc4FemqtewAfA99rrXvYfwE9MAIzlFK9gX6Av1LqYmuANkBr3Rq4HIgBlts9p+7Ai3bP6Qm7xwZb2+5v1/8SLYD3lFJbyn4BW4EbrPu9CmwHgoAvORsATgdMy9l2AAAgAElEQVTSgAjApJSaZNd2FDAKuAW433p9TmME9q2AcGuQvQP4DLjM+uEgUyl1u7WNMcBWu/5stQvszUB2meezDXgQGKW1Hg4cAibZ/TwCmAH8Yn+QUqodEKm1XkItaK2/AaKs7QghRCmuVe8ihBA10h3Ybb9Ba71XKXUAmIgRRH5gHbmrNa31VqXUMaANcDVGEJ2plFoAPGfd7Wrg31rrQusI6Y/Av8s09Rx2QajWOt/6bR/g2wrOqzECPYB7MEZBYzACUBfgcetjt2OMaGbYHd4TqDSg01qfVko9DSwEngZjFBbw11q3rew4O78Ai7XWxUqpH4AbrduvBsZrrS3W6/AssMjuvPuVUi9iBMWzlFKDgQNa6xusfTilte6hlBoA3Ke1vl0ptR9YZz1+GbDMviNKqb8ppaKBTsCzSql8wEdrPRlwAz4EPrO+hoHAKmCbUuoGjA84o4A1ZZ7fJOCTalyHc/kY4wPVK3VsRwhxgZGRYiGEo/UEhiilTimlDiulLrdufwMjYLwZmFXp0dWklOoGRAPHMEY7d4EtaC1SSvmU2Z4BhJVpIxwjSP+uglP0BqbbpU0cUkqtst9Ba30vRqC/ExgK7ADmK6X+wBi1vdSastDBekhP4C1rOsdy6/nL2oExOhto/bk1EF+da6K1XqS1Lrb+eAmwwfp9KHDA+v1xjBH1is5b0s++wG6l1DXnOF0PrfUBAOv1WauU2m79dxtwUGs9FeP1+RvwHyDA2s8cjBH634AXAC+Ma/iU9d94YLh1P3vdgU1ltj1tTf9YoZQKtfbnmFLqE+vv4K9lrvMGaztCCFGKBMVCCEfLxkhxaA7cBHyolPLQWi8HQoAVWuukarTzoF3u7d12219TSiUDvwMPaK0TMUZo7fN6czECsLLby94dmwJ8Yg2kbZRSvkAk0M0ubeJ5YF8lfQ0EHsYYBf4YI+A7prUeQun0ie0Y+cHh1raer6CtklQDX+u/IYC5otQJ61ehUqpVmf6HAo9hBKIAuXbPMcfa34rO6wugtX4TmAAkKKXcMIL0dIxR2h5KqcMYo+5Y9+8B3AmYtdaDrekpX9n1Pxnwo3QaRW+MkfbxGK/XjUAv63XsAzxVJp8ZjLSQtDLbijE+HO0F/mm3Pc36O7gNI6/Ztt3ajhBClCJBsRDCobTWc7XW863fb8AYmSwZmdtq/aoO+5ziD+22P4ORewtnA7M0Sgd6XoClgu32E8DAGLX+soJz9wG2lwmWL6JMUKyUGgN8aj3HWqCkekIgkGe3q4tSSmmt79VaH9VaW4D3gZEVnNvH+m82gNZ6k9a6T2Vf1v3iyrTxLvCp1nqP9WdLySQ6zl6bis5rC1q11lsxAtdwjNHVfwMzrQHwVxgpHvZuw8h77qWUesd6fdwwUiZyAf+S9q0pEi9hjKb/ALgD3wMewEMY+cThQLcy50jBCLLtfWR9nT7HyMsu8an13wXAALvtodZ2hBCiFAmKhRAOpZS6VZWuntCCioOwWtNan8QYgb3dumkLMNB6fm+MHN+UMttjMEYkS/rZESjQWpcNKMFII2hjlzqxA2PktexIcRjwX4wUgSDgSc6mBfRSSm3GSOGYDfRVSl1td2xl16UbcEprna6UelopFaeU2lfma79S6rRSKgxItg/elVL3Am0pPWq6CyPABSPgP1nJef+0a+cKjMl3/TFSLxZiTJYMB67HLidZKRWCMeI7H2PEdrz1+vbibF6w/Ujx98AYrXW81roXcA3GKPNzQGfra/KYfX+stgKDymwree4WSr+nlXwIMFH6Og/BGD0WQohSZKKdEMLRhgItlFJvAXdglPHafe5DauVtYI71PF8BvymldmFM0vpVa12klPoSmK2USgIepfQkt3HArxU1rLV+H2Mk18YaGO8rs9/n1lQLtNY/AD8opUzW8xwAjmitn7Qe7wUstk44PIMxka7URD6lVATwGkYFDbTW/6b8xED7/QdiBOQlPw/GGGXtr7UutNv1C+BNpdSzGCO0X5Rppx3wD+u5UUq1wLi+l1q3va+1PqyU2g1sxBjFT7Vr4j9Ac2AzRnrGcevz8wTmWfexD4q/BAKVUiX5zwoIUEZ5NqyD2gqjDN0Yu/MsAt4DvrbbdjtGrvpka99KTMGoNnID1gmBdtvvQQghypCgWAjhaM8B/7P+uw24vK6VJiqitf5dKZUNjNZaL1dKTQVexpikda91nzVKqdeBtzBGS+1zS4dxNmADbLf7zRXkGCuMEd+4kv201iX1epXdfgEYwXQCRjm4H5VS/8Qow5anlHoQ+Bkj1/krSpdeW4vxAeITjMlnlVJKtceoenE98JfdQ09iBJ/rrYGlWWsdpbWep4xSeO9hfBCwD/hPYIygv6m1Lqns8D/g/zBydWOAn615ysGAN9BcKRVpLd2G9Xm8oLU+Yu2fB8ZI7wigZEJhf4zSa2itLyvzfFyBBGs5tkppreOUUSd6itb6Y+vmSKVUAsYHlsl2u3sopU5Zr89k63nuwkiLqejugBCiiVNl/vYLIUSTpYxybj05e0u+MvnWEmVtMALrPRjl0F4G/qu1/j9re74Yo7JhwLAyo7d16WdPjAB/D/CG1jrZEe3atT8AY9T3FozR2m4Y6REzMQLmxzAmxK0GbqzgQ0RbjFHd0Rgjx5sxRs6nWidGlj2fF0ZQHFCNvrlilHO7t7LrqYwyb8O11sfstrljpLHcrbU2V3ScEKJpk6BYCOE01hG+shK11mUnWDVI1hHkTlrrP62jxEprnV7BftFa6xPnv4d1Zw1YRwLL7UbHS4LMUK31qUqOM1knFJ53FQXFQghRFQmKhRBCCCFEkyfVJ4QQQgghRJMnQbEQQgghhGjyJCgWQgghhBBNnlNKsoWGhurWrVs749RCCCGEEKIJ2bp1a7LWOqyq/ZwSFLdu3ZotW7Y449RCCCGEEKIJUUodr85+kj4hhBBCCCGaPAmKhRBCCCFEkydBsRBCCCGEaPKcklMshBBCCCFKKyoq4uTJk+Tn5zu7K42Sp6cnUVFRuLm51ep4CYqFEEIIIRqAkydP4ufnR+vWrTFWkRfVpbUmJSWFkydPEhMTU6s2JH1CCCFEg3IyLZd1h5Od3Q0hzrv8/HxCQkIkIK4FpRQhISF1GmWXoFgIIUSD8u6vh7h37la01s7uihDnnQTEtVfXaydBsRBCiAZDa82CzSfIKjCTkCl5lUJcaAoLC53dhUpJUCyEEKLByCow274/cCbbiT0RQlTm/fff58SJE6W2XXLJJdU69uGHH2bdunWlthUVFdm+f+utt1iwYIHtZ7PZzPkiE+2EEEI0GGk5Z0eRDp7JYlhslSuzCiEcxGw206ZNG9q0aQNASEgIe/fuxdfXl8suu4xp06YBEBsbyzXXXMObb77Jc889B8Du3bsZPnw4AF988QX+/v6MHj0aHx8fzGYzY8aM4bnnnsPV1RU/P79S5x00aBBeXl6YTCbi4uJo2bIl77//PlprcnNzWbNmDV5eXvX+/CUoFkII0WCk2AXFB85kObEnQjjf9bPXl9s2sVsktwxsTV5hMbd/sqnc49f2juK6PtGk5hRy//ytpR5beO/Ac55v165d3HDDDbz22msATJ8+nauuuoprrrmGQYMG0bNnT0aMGEFsbCwbNmxg/fr1DB8+nOnTpzNx4kSWLl1K586diYiIwGQysX792f4fOXKEG264gR07dhAfH09BQQF33XUXV199NWPHjqVfv34kJydz+PBh2rRpQ3R0NPHx8Rw+fPi8BMTgoKBYKeUKHLF+ATyktd7tiLaFEEI0HSUjxf+Y0JFxXSOd3BshmpYNGzawdOlSVq1aRdeuXYmMNP4Penl5MWnSJFavXk1MTAxXXXUVr7/+OiEhIeXaWLlyJSaTiVdffZVffvmF4uJiXFxc8Pf3Z/Hixdx1111MmzaNH374wTYx7rnnnuPYsWPMmDGDnJwc2rVrx8KFC/nxxx+ZPn36eXv+jhop7gZ8qbV+xkHtCSGEaIJaBHlx79A2XNmzBaG+Hs7ujhBOda6RXS93l3M+HuzjXuXIcFl9+/Zl5cqVREZGcuutt/Ljjz/SoUMHwEilOHz4MF26dGHVqlWcOHGC7OxsPv74Y2bPnk379u3p1q0bQ4cO5d133+XZZ5/lxhtv5JFHHmHRokW4uLgAkJycTFhYGFlZWbRr1w6Affv2sXDhQl577TXmzJnDvffey913301gYCDp6elMmTKlRs+jthw10W4AMFEptUkpNcc6ciyEEELUSIcIf/42viNFxRY+XXuUlOwCZ3dJiCajW7duttHhPn36cPDgQdtjqampBAcHAxAVFcXXX3/N6dOnmTFjBl26dOG3335j7NixXH/99bZj5s6dy9SpU5kzZw7z5s0DICcnBy8vL7KysvDx8SEjI4PZs2fz1FNP8fbbb3Pq1Clef/118vLyePLJJ0lMTDxvFSscFRRvBi7RWvcD3IDxZXdQSt2jlNqilNqSlJTkoNMKIYS4kGTkFpGVX0RcSi7Tv9/LrvgMZ3dJiCbjlltuYefOnRQXF7NkyRK6d+8OQEFBAV999RWjR48GYM6cOQD8/PPP9O7dm2HDhvHyyy+zceNGhgwZAsDhw4f54osviIuLY//+/bzwwgvs2rXL1mZWVha+vr4sXryYo0ePcuedd6K1JikpiVOnTmE2m3nkkUf4/fffmTt37nl5/o4a0d2ltS75OL8FuKjsDlrrD4APAPr06SMV2YVowArMxfzz2z95YEQ7ooO9nd0dUQcbj6Tw+KKdtAz2pmOkPxfHhjKifTNnd6tSL/6wl98PJrPskYsBowJFQ+6vEBeSadOmceONN6K15vLLL8fNzY2XXnqJd955h5tvvpnRo0ezceNGZs2axfz587n33nvp0qULXl5exMbG8u6779ryhIuLi7n22muJiIhgxIgRDB06lIceeoiPP/4YgISEBPz9/ZkyZUqp9Ig333yTiIgIJk+efN6fv6OC4nlKqZeAPcCVwMsOalcI4QRbj6WxYPMJTCbFy1d1dXZ3RB1sP5FOfHoewT7ufL7xOPnmYka0b0axRXP1rLW0CfOlY6QfHSP96RcTjIeri1P7m5ZbSJCPO0E+7oT6ekitYiHOoy5durBr165S28pOdPP19WXWrFn88MMP3Hffffzzn/9k165drFu3jo8++oghQ4bw5JNPcuWVV/LCCy/Yjlu0aBFTpkyhZcuWjBgxgpiYGFq3bl2uDwUFBee1NrE9RwXFLwBfAAr4Tmu90kHtCiGcIDXXyN+6ZUArJ/dE1FV6bhHuLia+e3AwFg25hcabTVZ+EcE+7qw/nMI32+MBuKRjMz66ra8zu0tKTiEhPu4AxIb7clDKsgnRoHTu3BmA/v37c+DAAVq1asW//vUv27aEhATc3d3LHff0009jMhlZu6tWraq0/WeecV7NBocExVrrPRgVKIQQF4D4tDzAqAQgGrdnxrbnwZHtUErhosDP0w2AQG93PrmjHwCpOYV89PsR5q0/TkJGPhEBnhW2lZJdwCvL9vGvyzvj41E/86nTcgqJCjJSdmLD/fhq60m01rZbskKIhiM2NpbY2NhS2yIiIirctyQgbsgafg+FEOddfLoRFF/1f2spMBc7uTeiLpRS+FYRwAb7uPPwqItY8/SISgNigJ//PMNXW09yJjPf0d20SbUbKX5k1EVs+vsoCYiFEOeFBMVCiHJiQn3w83DlcFIOhxNznN0dUQcfrDnMvA3Hq9zP082FIB93LBZNYiVB75nMfJSqvzsIWmsevSSWMZ3CAQjyccfbXSp8NkS5heZSS3ILcSGQoFgIUc4dg2P45oFBAPx1OtPJvRF18c32U6zeX/0ymA99uZ1bP96ExVK+SNDR5By0hu1x6Y7soo1SiilDYhjULhSAYovmlWV/8dOe0/VyPlFzxRbNws1xDP33Kia8/TvmYouzuyQuAOerDnFVJCgWQpSitUZrTesQH9xdTexLkKC4McvILSTQ263a+1/aJYJ9CVl8v+tUucdOpOUC8Ou+RIf1z15uoZlDidnkFxkpOy4mxTfb4lmxt37OJ2pu87FUnlm8Gx8PV05l5LPmoKw7IMBisfDWW2+RkpJi21ZcXMzYsWOrdfzll1/OqVOl/+bYV6B44oknWLdune3noqKiOva4YhIUCyFKycgrov3zP/H1tnjah/vx12mZ/d+YZeQVEeBV/aB4YtdIOkb6M3v1kXKPxaUYQXFSVv2sMrfjRDqXvLGabXFptm2x4X4cTJTfwYZiQJsQvrirPyseG0awjzuLt8Y7u0vCgTIyMhg3bhxjxozhqquuorCwkDvvvJOBAwfy4osvAnD77bfTo0cP+vTpw4cffggYk+giIyOZNGkSP/zwA8OHD2fUqFFs3rzZ9n1RUREHDhxgyJAhXHLJJQwdOpRPPvkEAFdXV/z8/Gz9OH36NIMGDWLYsGEMHz6cb775hueee47hw4czdOhQxo8vt0acQ0iylhCilJNpeRSaLfh7uXFp53Cy8p1TL1LUXaHZQk5hMYE1CIpNJsXIDmHMXn2EQrMFd1dj7ERrzfTLO/PQl9tJzKqfiXap1hzVEB8P27aLwn1ZsOkEFovGZJIJd8707Y54Ojf3t6W3vHhlF1oEls4vT80p5N1fD/HY6ItslU5E4/H555/z+OOPM3r0aO6//34WLFhAcXEx69evZ8qUKbZln9999106duxI9+7d6d+/P+Hh4YwePZprr72WL7/8kttvv53bb7+diRMnsnjxYgYMGICbmxuxsbH88ccftvNt2LCBm266iZ07dzJ16lTi4+N55ZVX6NevH3379mXy5MmsXbsWX19f8vLyGDlyJCtXriwVQDuSBMVCiFJOWm+RRwV5MbZLxaV1ROOQlV+El5tLjdInwBidNVs0R5NzaB9hvPkopbise3OW7jrF0eT6mXxZMnEryOdsf2PD/cgrKiY+PU9WV3Si/KJiHlmwgyfHxPLgSON3YnzXyHL73TJnI3+eyqRv6yDGVfC4qIFlz0LCbse2GdEVxr1a6cNTp061fZ+UlMT8+fN59NFHARgzZkypgDYkJIQJEyawZs0aOnbsyNSpU/n444/RuvR8BHd3d5YtWwbAo48+yt69ezGbzZhMJtq3b8/nn3/OmDFjmDt3Ls8++yzu7u4opZgxYwZHjhzhq6++IjU1lbFjx9KtWzeOHj3Ktdde68irYiPpE0KIUk5aaxRHWSsMWCxayrI1UiG+Hvw1Yyw313ARlsHtQvny7gG0tAtCT6TmsuloKoFe7iTWU/pESklQ7H228H9suC8BXm71WgZOVC0x03jNw/1Ll+zbE5/Bu78ao4daa/q2DgYgObt+fkfE+bF+/XrS0tKIjo6mRYsWAAQHB3PmzJlS+4WEhJCens6oUaP4+uuvCQoKIi8vj2nTphETE0N2djbt27dn/vz5gLGE8/PPP0/Xrl1ZunQp//d//2drSylFVlYWvr6+AGzbto1Vq1bx1FNPMWHCBIYNG8a//vUvFi1axI8//lgvz1tGioUQpZxMy8PXw5UALzeyC8z0f2klD4+6iHuHtXV210Qt1bTOb6ivB6G+HqW2Ld11mtd+2se6Z0cy/fLOjuyeTVpOIf6erri5nB2v6dUyiB3TRkutYidLsH4oKRsUbziSwuvLDzC2SyTtmvny/MROzNtw3La/qINzjOjWp9TUVB566CEWL17MG2+8QV6eMVCSnZ2NxWIpt29UVBRgLOTx/PPP4+fnx/vvv88HH3zAkiVLuP7665k0aZLtmHnz5vHEE08wffp0xo0bx6BBg3BzM+4OZWVl4ePjw5EjR/jpp5945JFHeOCBB0hISCAzM5OcnBxmzZrFJ598Ui95xTJSLIQopVerIG4b1Mq26IO/l5uUZWuktsel8eiC7bbFWGpi3aFkvt95djZ4XGoOwT7uNA/0wsvdxZHdtLmiZwv+dUXpgFspJQFxA1AyUl92cZcrerTAxaS4b/5WftpzGheTonWIN+bi8iX9RMNXWFjIddddxyuvvEKrVq3o3bu3LWVi586dtG7d2rZveno6y5YtY+TIkQC88MILREdHs3LlSgYOHEhYWBhvvPEGFouFli1bArBx40a2bt3Kb7/9xunTp/nnP//JihUrGDduHIBtpHjBggX8+eefPPTQQ5hMJlJSUjh27Bhms5l77rmH1atXs3TpUoc/fxkpFkKUcnn35tC9ue3nDhF+7EuQ2f+N0eGkHJbsOMXjo9vX+NjPN8ax51QGl1l/F46n5NIqxJvjKTnMXX+cWwe2olWIj0P726tlEL1aBpXb/v7qw/x1OpO3Jvd06PlE9ZUExeF+pYPiMD8PhseG8cu+RNYeSmFsl0h+eWK4E3ooHGHOnDls27aNl156iZdeeok77riDefPmcerUKZYtW8aGDRtYvnw5Dz30EB4eHrz22mt06NCBb7/9lg0bNjB9+nRWr15NUFAQN954IyNHjiyV6mCxWLjmmmto06YN48aNY+XKlbz22mssWbIEMPKYfXx8eO6550r169FHH2Xy5MkMGDCgXp+/BMVCiFJSsgsI9nG3jc51jPTn94PJFJiL8XCtnxFCUT/Sc40c3YAaTrQDY4Lbj3tOk1toxtvdleMpufRtHUR6bhFz/jjKoLYhDg+Kt8elEeLjQcuQ0hPqEjML+PnPBKlA4UTX9o6iT+tg/L3Khw33DmuLBp4eW/MPX6Jhuf/++7n//vtLbbv88stZsWIFTz/9NAEBAXz66afljgsICGD27Nm899573HLLLTz++OMkJiayadMmZs6cycyZM5kxYwaDBw9m4MCBtuO2bdvGtGnTsFgs9OvXj9GjR+PiUv59pqCgoFTd4voiQbEQwiYjr4jeL67k7+M7cvfQNgB0iPTHbNEcTsyhU3N/J/dQ1ERGXhEmBX4eNf9T3z7CF63hUGI2HSL8OZ2RR8uQKML8jFzjutYqjkvJZejMVcy5rQ+jOhrLOt8/fxtDY0P597XdS+0bG+5LfpGFE2m5Dg/ERfUEervTw24CpL1+McH0iwm2/bxkezzfbI/n0zv6SurLBSAoKKhUTnBFhg8fDsDMmTPZunUrt99+O926dQNg0aJFHDlyhObNm5c77u2338ZkMjJ5N23aVGn77733Xi17XzOSUyyEsIm3Vp5obld7tFfLQB4edVGFI0SiYUvPNRbuqM3oamy4UXZrf0IWJgX/u28g1/RqQYivERjVtQJFkrU6wdz1xwGjckFqbiFBPuUDr4usfTlwJrtO5xS1t3jrSVbtr97KgklZBaw+kERWgdQ4b4p69+5tC4hLtGnTBk9Pz3L7lgTEDUXD6o0QwqlKJmSVlGMzvvfm8dGxRAV58/ySPXy+8biUx2ok3FxMpcqq1UQr6zLfBxOzcXUx0btVMK1CfPBwNeoe13WkuHerIPq1DiY9z1iuNaewmEKzhZAKg2KjRNOBM5Lb7ixv/3qQb7ZVb/W6cOtkvIQM+TtRG2Xr/Irqq+u1k6EfIYSN/cIdZWXlF7HmYBLzNhzn79/soVtUAKM7hnN5j+ZyS7uBmnZZp1of62JS/PrEMCIDvNhxIp1jyTlM7BaJq4uJMF8Psus4ClhottA3JojZq4+QW2g+u3BHBbfo/T3dGNIuFN9apIGIutNak5CRz5hOHlXvDETaBcUldxxE9Xh6epKSkkJISIikntSQ1pqUlJQKR6SrS/7CCCFs4tPy8HQzEVzBaJ2fpxu/PTmcg4nZrNh7hhV7z/CfFQeICPCkVYgPSVkFnEjLrbB6gGicooKMUeZvd8SzcPMJruhh5AQue+RiXF1qf6Mxt9BMrxkrGNIuFLNF89fpLFytKR4l6Rllzb+rf63PJ+omM89MgdlSrkZxZSL8ZaS4tqKiojh58iRJSUnO7kqj5OnpaaubXBsSFAshbEZ0aEZkoFelIxRKKWLD/YgN9+OBEe1IzMrH2934M/L9zlPM+GEvS6YOpnt04PnstqjEYwt30LVFAFOGxNTq+D3xGXy5KY6DZ7JpGext+72oS0AMsOtkBvlFFq7tHcXMa7sT5ONORm4RH9zSm64tzv27o7WWEbTzrLKFOyoT7u9J2zAfXF3kdaopNzc3YmJq9/9V1J3kFAshbAa3C+XOGgRQzfw8bbe0L+veHD8PV97+5WB9dU/U0K/7EjmeklPr4xOz8vl8YxybjqXSyq5M2s9/JvDIgu3Vyt9btS+R6d/9WWrfbXFpAPSPCbFNrAvwdmNM5whbdYuK2uk9YwXHUnJr/XxE7VS2cEdl3F1N/PLEcK7uVfsROyGcQYJiIYTNn6cyyMovqtWxYX4e3H1xG37Zl8jukxkO7pmoqWKLJjO/iIBKymhVh30+qH3e+PGUHL7dcYqcwuJzHl9gLuaOTzfz6bpj7LT7ndgel05MqA9BPu78fjCJe+Zu4a/Tmazan4jFUnGgHejtRkpOoUy2c4LB7ULZ9NwourYIcHZXhKhXEhQLIQDIKTAz4e0/mLfheK3buG1wa/w9XXlLRoudLiu/CK0h0KvmC3eUaGFXms9+pLhkNDexiiokHq4uLLrXKNS/cHMcYKQ/bI9Lp2dLI00iPbeI5XvP8NIPf3HP3C1UlhlRUpbtUKKUZTvfXEyKZv6eeLpVf/GemT/v467PttRjr4RwPAmKhRCAfTm22pXwAqNKwJ1D2pCUlU9+0blHEUX9Sss1RvyDfGofFCul6B4dSKsQbyZ2PVt4P8zXuI1+rrJsGdbz94sJ5rreUXy34xQ5BWbMFs09Q2O4okcLAPq2NhZ9+ONQMkHe7pXmC/t6uNIi0Ms2Urz+cAoPfLGNVGvVCofaPAd+ecH498DPkJfu+HM0It/tPMVHvx+p0THpuUVsPZ5aTz0Son5IUCyEAM4u3GE/OlgbU0e0ZckDg2s0qiQcz1xsoX24H+F+tS9PBNAp0o9AL7dSS0XbVrXLrjgozsovYuxba3hz5QEAJveLBsXcUfYAACAASURBVGDv6UzcXEzcM7Qtw2LDACNPNTrY+J2rqOqJvYvCfTlwJpvvd57ixo828MOu02w6mlKn51fO0TXww+Pw+3+Mf7+YBB9dAua61WVuzL7feYr/bTlZo2MiAzxJyy2SD8eiUZHqE0IIADKtucRB3rUfWQRjwQgw0jGUwladQpxfF4X78fNjQ+vczktXdi23Il6YnwchPu4UFVsqPOa1n/aRkJnPUGvg26tlEJv+fgk+Hq7sT8gi2Me91IS6vq2COZEaf7ZGcfJBiN8GFrPdVzGP+CQT7+bD0Nj+3NCvJV9sjCOlZKQ4PQ5cvcA3rPZPtrgIfnwKAlvC/euhIBOOrIYl98Hat2HYU7VvuxE7k5lvW5CjuiICvGzHSh1z0VjIu5UQAoCCIiPAcXet+w2kE6m5XPzvVfz7mm5M6htd5/aE81S0RHSwjztbnx9d4f6bjqYyf0McUwbH2GpWK6Xw8XBFa82jC3fg7+nKQmuuMcDAtiF8vT3+bAmvL2+AlPJ56T2tX6QOYfplPY2gOLsQshLgnT5QXAC+ERDRFSK6GP+Gd4WQtmCqxp2LjbMhaR9M/gI8fI2vHjfAgWXw++vQ7ToIam3sm5UAuxaCizt4+IFPM2g7ElwuvLfVM5n5dIio2SIcJbWKT2dIUCwajwvvf68Qolb6xgTz+nXdCfGp3qpV5xIR4ImLSXE8tfblwETdfLsjnrnrj/PpHX3x86zb6H915RcV8+ziXUQFefHkpbGlHiu2aMa/9Tv7z2Rx37C2pR67rk80nZr74+5iguRDRkA88h/QdRKYXM9+mfPg7V6wcyHu43vzjwkd6dUqCHbNNQLi4X+DtGOQsBuOrDJGmMEYQQ7vZA2Su0BEN+NnD7tALysBfnsV2o2G9uNLP7FLX4GDK+Gnv8ENX0LcRlh0K2QnlN6v22S46n0qnS3YCJmLLSRlFVS7RnGJlsHeDGkXartzJERj4NCgWCkVDvykte7pyHaFEPUvJtSHmFDHjOi4uZhoEejFcakp6zTHknPZejwNr3rK7X512T6KLRb+PuHsUtL7ErJIyi5g1k29yqXNuJiUbWno7lHlS3t1bm7dtn6u8W/XSRDUqvyJ24+DPV/BpS9x18VtQGv4/guI7g/Dnz27n7kAkvbDmT1GkJywG/5cAls/PbtPcJuzQfLJzUZgPe618kFtQAsY9jSs/Cd8/whs/xwCouC+P8C/BRRkwfZ5sGYmBEYbAf0FIjW3EJNSNQ+KQ7xlFULR6Dh6pPh1oG6zdIQQTnEiNZczmfn0bhXkkBXDWoV4E5cqQbGzpOcV4ufhWufV5ypz8EyWbaWzEj2iA/njmZEEVFIGbtZNvZj+/Z8MahtaecMHfoawjhUHxADdb4C9S+DQSs5EjqDw+Caik/bBZW+V3s/VAyK7GV8ltIbMeGuQvAcSdhlB81/fGY8PfcpItajIgKmw4wsjqG53CVzzEXhZlzT3DoYRf4fsM0ZgHBAFvW+v/Dk2Is38PDnw4jjMldSPFuJC4rCgWCk1EsgBEqraV9S/1JxCvth4nLTcIh4Y0a7KWd1C/G/LCd5ZdYgjL4+veudqaBnszdJdpx3Slqi5jNyiUhUjHC3Mz4Nd8caCHOZiC8v3nmFcl4hKA2KA7tGBfDN1cOWNFmTB8XUwcGrl+7QbBd6hsPNL/rmpGRNOzCLa1Qs6X1V1p5UyAtaAKGPE2f68qUeMUePKuLrD9fOM/vW6tXyOslIw4Q3IPAVLHzdGkC+qOO+6sTGZFO4V5JZX5fZPNhHg5cZbk+XmsWgcHDKEoJRyB54Hnj3HPvcopbYopbYkJSU54rSiEhaL5vJ3/+D15Qf4dN0xxvx3DSv3nnF2t0QDV2C24OFqcsgoMcCEbpE8MSa20hXKRP1KzysisJ6D4pTsAootmo/XHmXq59tYd7iO5dEOrwJLEVx0aeX7uLhB12th/zJaemQxrHANdLwMPOuw2pqHH0R2r3oyXlh76HNH5fu5uMF1n0J4Z1h0G5zaXvs+NRCr9iXyt693kVtorvGxxRYty3KLRsVR99WeBWZprSutcK61/kBr3Udr3ScsrA4lc0Q5WmtWH0jiiUU7MRdbMJkUL1zRmRWPDWXpQ0MI8/Pgrrlb2HmiaRegF+dmBMWOyz8d1DaUWwe2rrB6gah/rUN86BkdVG/th/l5YNGw40Qab6w4wOhO4QxqG1K3Rg/8bAS30VXkonafDMWF3BT/Ev7kUNz9xrqd15E8/OCm/4F3CHw+CdJqv0JkQ7DleCqLtpys1d+GCH9PzmSce9VDIRoSR6VPXAKMVEo9APRQSn2ktb7LQW2LSuQXFbNkezxz/jjKwcRswvw8OJaSS7tmvozsEG7b79sHBrN8bwLdowOd2FvR0BWYi/FwQDm2EsUWzdHkbPw83Wo8SUfU3bTLOlW9Ux1EB3vTKdKfRxbswM1kYsYVXep2l8FigYPLoe2oqsuaRfaAsA60StpEvA7Bs1l/6hiOO5ZfBNz8FcwZDZ9fC1N+NvKOG6EzmQWE+XrgUosPt5EBniRm5WMuttRbbrsQjuSQ31Kt9VCt9XCt9XBghwTE9e9ocg6DXv2VZ7/ejauLidev684fz4ygXTPfcvu6u5qY2K15Ba0IcVZBkQUPN8e9cRWYi7nkjTV8tbVmK2GJxmFE+2bcOrAVJ9Py+Nv4jkTUcHGHck7vgJxEiB1b9b5KQbfrAfi6+GJS8hrgqmlh7WHyl0aJuIU3N9oV8WqzcEeJ8ABPLLrylQ+FaGgc/tHNGhiLetYq2JtLO0fwxd39+fHhIVzbO+qct7f+PJXBqP/8xqajsha9qNgdg2N48cquDmvP292VMD8PjqdIreLzTWvNsJmr+GzdMcc2bLHA8fVgMYLQ5oFeXNWzBZPPtUCLuRDyM6pu++ByQBmVHaqj163kXnQ57cY/QjO/utfWrhetB8OV78HxtfDNfcb1a2TOZOYT4V+769sp0p9re0c5uEdC1B9ZvKORMpkUr1xd/QDGpBSHk3JIkU/sohJdK6gdW1etgr2lVrETZBeYOZ6SS6HZwUHYjs/huwdhxD9g2FMMjQ2zLeVcvhNJsOVj2DIHtAUe3QNu5xhxPPATRPUFn2omQviE4n3TPMZVvadzdb3WKAO3YppR9WLMDGf3qEZMShEV5F2rY3u2DKJny/rLaxfC0STJpxHaE5/BrR9v4sCZrGof4+dpfP7Jyq/5DGLRNGw9nsYOB0/GbBkiQbEzpOcWATi+JNs268Iav70CcRsq3uf0LlgyFf7bCX57GfwiISfJWGGuMskHjUoNHWpWDrDYotl8LLXh340Y9DD0vRvWvQ2bPnR2b2rkp0eH8vzE2uena60d/+FMiHoiQXEjdCo9jzUHkmr0h8bfWjs0M7+ovrolGrnXlu3jtWX7HNpm6xAfEjLzyS9qgDmfF7CMPGtQfI6awTWWtB9OboJhzxirti2+C/LSjMcsxfDX9/DJeJh9Mfz5jVHL98EtcOcK8AiAvd9V3vbmj8DkBj1uqnG3Js1ez+KGnreulLFSXvvx8NOzEL/N2T06L7TWdP/Xcv6zfL+zuyJEtUj6RCNUslRqyehvdfi6u6IUZMpIsahEgbmYIAcv8jK+awQdIvzKrZor6ldJUBzoyKB4+3wwuULfu4w6wh+PgW8fhJYDYdNsSI+DgJYwegb0uuXsam9gjADv/8HIL3Yt8ztWmGOsFNf5SvBtVqMuuZgUwd7uJOcUOuAJ1jOTC1w5C94bDF/fA/euAffapSWcLx+sOczmY2nMvrl3rUorKqUI9HbntJRlE42EjBQ3QiUpEL4e1Q+KTSbF6I7hRAfJKtyiYiWLdzhSu2Z+jOkc4dD6x6Jqvh6uXNIxvO4VIUoUF8HOBUZlCN9mENUbRk2DfUth+d/BPwomzYOHt8Pgh0sHxACdrjAm2x1bU77tXYugINNIL6iFEF/3xjNXwivICIxTDho5xg3c7weTOZmWV6da4xEBniRIUCwaCRkpboRKRop9azBSDPDBrX3qozviAuHoxTvAWF3xj0PJhPl50DHS36Fti8p1jw7ko9sc+P/94HKjXFrPm89uG/iQEeRFdIPmPc59fJsR4O4Le78tXV1CayN1IqIrRPerVddCfDxIbQwjxSXaDIcBD8CG/zM+ZFxUzWob55nFotl5Ip0JdSznGeHv6fC5Co6WmJnPqYx8ekgt/yZPRoobIV8PVzpE+Mnom3Co/CLHLt4BRirl1M+38eWmOIe2K+pRcRHs/sqoHFGQbWzbPh98w6Hd6LP7mUxG3nBVATEYVSdix8JfS6HYLoUrbgOc2WOMEtcyx8YYKW5EQTEYo+xhHeHbByCvYQaMx1JyyMw30yO6blVpIgM8ScjMR+uGu9x7Sk4hjy3cwR2fbOJgDSawiwuPjBQDpzPy+DM+k4tjQ/FwdeFkWi5JWQW4uZhwdzXh5mLC1aRoEejVIJasvW1Qa24b1LrGxz3w+TYKzBbHjiCJC8Y7N/S0Tch0FKUUraQCxXk38+d9fLfzFL8/PbJ6B2gNuamw80vY8B5kWieurfwX9P5/9s47PKpye9v3np7ee0hCCIROCL2DgoINFAS72LCe47GXo0fsHv199u6xY0MRRUWaDZBO6CF0Qnrvmcm0/f3xzqSQPiUEmPu69pVkZs87e5Lsvde73rWe53phvzz2H+07zbVF/5mw9zuh2Zs4STy29QPRhDdojsPD3japF/rTrZFTrYNL34UPpsAfz8EFL57qI2qGPbvrrBPqhN5haFUKTBYZjerU3z9boleYL1eO7MEbvx1m+mvruGpkHP+a2psQ326qf+3BbXiCYuC+xbvYcKSEHY9PQ6tS8sXmE7zz55Fm+x14ZjpaxembnTWYLORXemq7PLTM8AT32NDGh3izP8+TfelK9uZUolLYsv4WM2Rthqo8IY1WXShKIaqLmn612LKt8ePhopdFacTfr4kNmpZOOELSVFB7ixKKmFTY/qlQpBh5C2h8HB52YIzr9bW7hOgUGH6TmBgMvQaiBp/qI2qCt0bF+KRQeof7OTXO+N6hjO8d6qKjci2yLPP6b4e5LDWGBRN7MTs1ltd+O8QXm0+wbFcuax+c4loFFw/dHk9QDBwurGZqv4h6NYfLh8UyMiEYk8WKySJjslgxWqyoFQosVpms0loSQh2/iDvLwmX7MJgsvDC7cxdRP52KQ4Ue9QkPLfPjzhz6RfnTJ8K5m+DJxAX7sDq9AItVRtkNVlrOdIqr61h/uJhbJiSKB7Z9CL8+2LCDQgU+YWLzDRfL+L5h4BMO8WMgZljDvld8IaTYyrMgtLdzB6bxht7TRGPdnu+grgISJsC4fzk1bH6FgS3HS5mSHIaf7jQLYM75t5CvW34/3LBClKR0E6YPjGT6wEinx5FlmdIaIxqVosv/PrIs89v+QiYlh6FWNv/d/ra/kFfWHCQqQEePYG9CfLU8NXMg141JYOPRkvqAeHtmKalxQUgeGZ0znrM+KNYbLRRW1TEkNgCV7aRJDPMlMcy3xf2f+HEvP+3O48c7x9Ej+NTI6ezLrWjIAnUCfy+1R6fYQ4tYrDJ3f72Te6b2cXlQHB/ijckik1uuP2XnTJdjNgqFgfD+DtfKOsryPXlYrDKzhtoapDJ+hpDeMG+RCIJ1gZ0LvsKSxeYKUq4Wesb9LhEqFY0DcAfZmVXOP7/awc//GH/6ZY29gmDaU/DjHaJ0ZWjndZrdgcUqY7a6pvE2v9LAmOd/59lLB3L1qHgXHF3HSTtRxs2fbeOxC/txs32SaEOWZV5efZD4EG8uTY1p8lxSuC9J4SIG2JtTwex3NjI0LpDHLuzPsHiPQ9+ZTPeZlp4iTpSKWse4kI7drK8fm4DJYmXB59upNZ6arGuVwdwpjWI7fjoVVQZzt2548HBqsBvBaNWuvyRM6x/Bz/8YT4S/i+TBTgd+uhveGQtvjxY1urWlXfbWP+7MpW+kH30j/YUMWuYG6HshhPcF7+BTm43scz48VghzP3VJQAwQ6it0j0tOJwWKxgy5EnqMEhJte76DipxTfUTsyalg4BMrWX+o2Omxwny1KCROiSxbUhulHyv3FZCeV8k/z+ndYhbZTr8of16cM5icMj2z39nAHwcK3XGoHroJZ31QXKE3EeGvJSGkY+UQiWG+vHHlUPbnVfLZxkw3H13LVBnMnZZjAxgUE8islBjMVk9Q7KEpdWbRqORq9QmAUF8tA2MC0Lhh7G7JwZWw60vRWKbxFQ5mrwwU9sddwG2TenHvtD7ihyO/g9UslB+6C0rXLqEH2wxnSmtOE63ik1Eo4MKXxfdLbhL22K8NgaN/nrJD2nmiDJNFJjHM+TJBlVJBmJ/2lATF/joV3hpli+YhH64/Ss9QH2amtC05p1RIzB3eg9X3iObQ/XmVbjlWD92Ds758YmTPYDY/2jmdyMnJ4fhpVadMkLy6zoxfJ4w77LiqRszDmUedPVPsJpm/r7ecICHUh9GJIW4Zv9ugLxNZ4vABcNn/hHtb/h74bCaseATm/+z2copp/SMafjiwQizRx45w63ueSuwKAaedLFtjIgfC/QfF/0rmBtF89/0CuGOTyO53MbuyKwj30xLlIvOXyACvLm/ylmWZ819dS63R0uK9OjnSj2nB3vVlk+3h7yVcYfXG00zpxEOnOEtSN64nKlCH4hQV3Q+KCaivd3IET/mEh5OpM9mDYvdcEl5aeYBlu3LdMna3YsWjQt1h1tsNdsaRg2DKo5C5XtT3uglZlvlw/TFO2OXvrBZhutH7POek1Lo5/joVaqVE8ekcFIOwgY5OgTF3wJyPoKYYVj56Sg5lV1Y5Q3oEuqyxLNK/6zPFWaV6DhYIne28Cn2z55+ZNYgFE3t1eDxJklj5r4ncMK6ny47RQ/fjzL1SdpDHftiDj0bFIxf069TrVtmWUk4Fi24e5dDrNhwuZv4nW/nqllEMi+/67IOH7ku4v5Zld40jJtA9NuDBPhpKT/egpT3sZRMTH8AaOYQ/9hcwPCFYdLCnzoctH8Cqx6H3+Q0BswtJz6vk6Z/T0aoUXBMSD9nbQF8q6njPYCRJ4tvbxhLtKkvr7kD0UBh/D6z7PxhwafO/odUiVEFy0yAnTXytKoAbV0CQc81sFbUmjhbXMHtYrFPjNOaKEXFd7jqYdqIMgMcv6k+/yKa1xbIsOxTwu7oJ+XTFapUprTWiPQWKIu7mrA+K1x8qZsDp1rHsIFq1EqPZSqXBI8vmoSk6tZLBse6zOA320ZxeVrwdoKDSwMGCKg4XVpOVm8td+2/HqOtF5MQHUSgkHvxuN+V6E0N7BHLegAhumvo0yq8uhy3vw9i7XH48y3bmolJIXDgoSjxwcAVISuh1rsvfq7txOtrzmixWDhdWkxzh17Ip1KQHIeMXUY5z9be2IHiHCILzdoGpRuyn8YOoIVCVC/u+F8G0E8jIPDS9L5P6hDk1TmOm9A132VgdZceJMrw1Sq4fE9+sRGLtoWJe/fIH3h1eSETNfvH7rC4EtZfQ0lZ7CwlBtY94zPb94UqJ0qTZjBw/rZV3PbORZZmccj06tZLhz6xh4cX9mX+GZc7P6qDYbLGSXabnAvtNpBO899cRcsv1PDlzoBuOrHWOF9dwwydbWXjJgE5ftPxtzXlVnqDYw0kUVhpYs7+Qc/uFu0UlIsRXw4H8M8vA44aPt5Jua7p5Tfc+AZSxuO+rXGnLAn9w/XD+yCjkjwOFPLc8g6A5g7k8aSr89aJQHPBxbX31ukPFjE4MIcjWeMbBlRA/FrxOv4Cxs/x9uJiiqjpmDY1pf+duwmcbM3lu+X4OPD0dBRJZpbVNJQtVWlGG87+p8O5422M6UY4z9BphgBKdCiFJolnv/SlC7s7JoDjQW8Ptk1spK5BlqKuC2hKx+YR1KDOtN1o4UlRNQqgPvg70wzhC2olyhsQGUqE3sTOrnFGJIfXvLR1YztfyI2i3mSA4USihBMSCyQCmWrEZbV8N5VCZC6YaosuLSDz+DdTcIUqinDCdOd2orjPz4He72Hy0lF//NQFJgtLaM0/i9awOinPLDZitMvEdlGNrTHpeJTtOlPPkyU/s/ApKjwpRdjdQVmvkWHENVgcUJOwWvpX6M+8f2YNzHC6s5tGle/gmbLRbguIuyRSvfUmcf30vFLbBkYNd3tRWWGUgp0zPoJgAHrmgL0qFxIDqTQQs/RMmPsCV51xSv29qXBCpcUHcO60P57+6lq+2nODyOc8Imbadi2Dc3S49tuyy2gYN1fITULgPznvWpe/RXfluezZbj5c6HBR/uP4Yn244ztOzBro0Q9oWq9PzUSslVEoFtUYzV7y/ideuSGnqLBmTCvM+F1nMmFShe92aeke/i+C3p4SkW4Djk4OdWeXEBnkRerLFsdUKX86Fw6sbHvMKgn/ubHfitSu7nCve38Sim0Z1mbvdhN6hxAV7szOrnJs+3cbSO8YyNC4Idn3N+O33sFtOYOC9v6AK6HhS7MY3VnNL3aecu/FNMQGZ9JAobfHpno59ruJYcQ0LPtvGkaJqHr2gH2G+WgK91Kev4ksbnNWNdpmlYvmpj6oQ9i6B4+uh+BDoy8WMuA38dWqqTjbCOPybEGH/+zVhreoGquvEuI5Isvl5MsUeWqFefULtHvWJe6b2cW8d/sa34fdnhFvbprfhvYnw1ijY9pHI/nQEWYaakjb3X747j0vf3kB+pYEJvcMYG60iYM39IliZ+ECLr5EkiZfnpvD+dcMhvB+E9YVjax35lK1SU2emxmghJshWE35ghfjanaTY3EiIj6ZT6hNWq8wfBwoxmISSgEKC0hojL6864FAjcpXBxPyPt/D34Y7p+lYZTGw7Xsb8sWLp2WCyklOuZ+vxsuY7970Qht8gSiTakrPrZ5uQZfzS2cOvR5Zlbv50K88vz2j+5I7PRUA8cgHMfBtmviXulX+/2u64kbaJdqsKFGmfwYu94IU4sb0ySMgJOsGD0/tyxcg4EowHOE+xFWnvd2LSsPRWDnun8IDX050KiAHQ+vKe750wfzkoNeJ+/1ISfHgebH4fzGdekPh7RgGXvLme4uo6Pr9pFDdPSESSpDOyJA7O8kyxQpK4MjKHIctvaajPsqPUgm+EsD/1jRDLRP7RwqY0OrWJEYYkSVByBL67QZwoZgOUHnGdC1Qj7AGtI+YdXmolV4+Ko1+Up1nAQ1PcqVMMDbJZTSg6IGTKSo9A0jRIniFsfzvbhLbzK1j5iAgKLv9E3Kj3/yhutD/fA388D6Nvg+E3tZ7Rslrg2+tF9gfEeRzaR9Ry+jfomG49XkZ0gI7YINvq0kqb2sSVX4nl7lZo4rTWcyLsWCRc7zr5WStqTcx7fyM3ju/J3OE96h/30ao48PR0oUFelQ9rXxSZ8tCkTo1/uhLiq0VvslBrNOOtaf3aWFZjZPG2LBZtziSrVM/Lc4dwWWosN4zriUqp4PEf9rLlWCmjOikduD+vij8PFLHpaAn7n5rebhPX34dLMFtlJieLrHSwj4aYQC/25VZ06n2bENobQpNh/zIYtcChIXLK9RRXG0mJO+k8qS2FNQshbgzMeLFhBebYWmFOM3JBk/PkZCJtTZD5LahAkL8HfrlPlIXEDBdjH/0LvrhcBN5Druj05yisMhDgpUabvoReS2/hfQ2wxfZkv4tZWHYLQXS+2dVbo6KwyiCuU3dugfxdokwp4xf49QHY8IYoqxg8V6iJnAF8szWLuGBv3rt2WMN1Dwjx0Z7eMoitcFYHxePUhxhXsxD8o2DWuyIwri60bQXia02hWIrM3gY1RfDn8xDSm0n+09gqh2IoS8HLyxu+ukJkqWa/Dd9cDQV73RIUV9uCYkfqsiRJ4tlLB7n6kDycATToFLsnKD5eXMP3adlcNSqeSJ1J1NVuelvU5PUYDTu/ENqsGj/oPRWSLxATUK82LFUr8yD9B1j5b0icDLP/J25EPiEw/EYYdoO4af/9qsgQrXsFhs+H0XeKc74xqx4TAfGo28VEWF8OW/8HP94JVy8BhQJZltlyvJSxvWwB08FV4rgn3C/UAtphb04Fz/ySzrvDxxBoeh9ytkP8mA7/DmVZRqdRcLS4hsOF1c2eVykVqDDDdzeCsQYu+6DDY5/uhNhd7aqNeAc3vzYaTBb+vXQvP+3OxWi2MrJnMA9N78t5/Rt02+ekxvLK6oN8sO5op4PiAlsG9IubRyNJEkaztU2zmr8OFuKnVTWxDB4Q7U96rpPGEP0uhvWviBUPB2rWd2aVA5ByctPtmoXCHfHC/9e0JGnKv2HfUnFfvOSNVsfVqZUEeaubZ4pNelhyszjPr1rcUIZgqIBvroGlt4p63vH3dKoU6rGleykvzGax+QHk2JHMOn4pFwztya1TB4F/DBPXHsXLgVUxL7WSWrtOsUIhzvvooTD5YZHZXrMQfrhNBMdTnxByiO0dtywLfXOtf7eRTqw0mKitsxAZoOP/zU1BpZDQnfT7un5sAjJnnrxr9/gLdAUWE5RlCncnqxnKjgtxdP9oIajv1wFTC305pP8Iu79h1LG3+VYLvP6UCIYBrvtRiORLSihIh4GzXf4xwvy0TE4OEzJPDmCxyhjNVrw0Z8Ys1oNrqNcpdlP5RG6Fnrd+P8Al1tWw9w2ozhfNQlOfFDdCk15khw78Ipb+9y0V51H82IYAua4KCvdDwT4R7BbsEYPHjYF5XzTP1EoSJE4SW94uUda08S3Y9K7IPo27W2TXtnwgAvTRd8D05xteH5QAv9wrguNRC8gsqaWoqo6RPYPFteCnf4qyiUkPduh34KNVseloKV/FxXG7pIBjf3UqKE47UcZti9Iwmq0NWsQ2fs8o4Lf9hSz0WYI682+49H1h63yWEOLTYPVsb1bTGy2k51UwLD4YrUpBVlkt84b34JrR8SRHNl8t89IomT82gYMFVZgt1g6bOkBDUJwU5oveaOGaDzczLimUe6b2bjFrfNukXkztF9HEch0j0gAAIABJREFUXnhAdACr0guorjM73ozW72Ih43bwV3F+dZJdWeVoVIqmv5/sbWLVZfQdEDGg6QuC4mHEzbD5XRhzl0gE1ZZC9lbh5ugbLlZZdQFE+OuaaxWvehyKMuCa75vW5eoCxGT0xzvgtyfFxNYegEanivpq/5gWA05ZlknLLONjrw/ApEea+Rbln+Sw1xgomukQv39HeHpWG431vc6BnpMhfSn89rSov44bCxPvE9eyqnxx3avKh6o821fbZqkTf7t5ixw6LldytKiamz/bhp9OzQ93jG31f/HCwZ0XKDgdOHuC4qp8eHNYk4dylLF8E/My93YkIAax9DrserFV5kJhOpQcFY11vc6BBFuHcGgfceN2A1P6hjslbzPzrfWE+Wr5+IaRLjwqD6c7MwZFkhofRLhfCyUAJgNsfgfqqiEwTtwIA+PAP7b95X+rFSpOkJC3nhWa50jalAM9RomLf49GLmtqL0ieLjarVeiuZvwCB34VpRErH2nYV6kVk8+pC0XZRcSA9rMxUUOEIcI5j8PGN0X5wo5F0GuKsNPtMwPOe6Z+9z8PFPLdwRRe6zUV5erHIXESWzJFsDUyIRhWPtyhsonG9Az1YXJyGB9tL+fWyCEojq0VGaYO8vnGTAxGC6MTg8ksbRoUbz5aStH2ZahVr0Dq9TBkXofHPRMYnRjC+oemEOGv43hxDYs2ZfLt9mxMFiubHj0Xf52abxaMbres4R/nJDmkX1tQaUCrUuDvpcJilekV5sPrvx2iuLqOp2cORHmS5Fp8iA/xIU2VC8YmhZBfGYfeaHE8KI4aAgFxYtVj6DVCQWHfUnHOdaCUZmdWOQOj/Ruy3Ca9KEHyjWj9f3XC/ZD2OSy9TUiZndgI8kmub0otP2iDsVSFwZfRIlBWacWEc/SdkNSCbKBKIyZ3vc+DzL+FFN2GN0RSC8QY9gDZHiz7hpFdpmes/k8GWdbDtKcgrA+vzAsj2Ftcq0wWK3Vmq0O/Y7uleKsoFCIZ1u8SSPsU/vwvLDopOab1F0k43wjxd/GLFC6GWVs7fTzu4MUVByiqquO5Swe1eS5U6E1kldbSP8q/ZUnB0xSXBsWSJAUDw4Adsix3rOOgq/AOEbarCiUolMgKFZd9aeACtYOySP7RYmvpOhPR32X/4GaLFYUkueyfzk+r9jTaeWiGn07dsgh7YQYsuUmUA0nKk252kjgHAuMg0BYoewdDRbYoOSo7LhpXzXqigSNE8UfKy0yZeWPbQaxCAbHDxTb1CTHpPPoneIeKzGxwT8fr9YJ7iiXgSQ/DlvdEljhyEMz+H9+m5RLhr2NinzAMJis/78ln6gWPMit3Dnx/C5ec9yIJ1ybRq3yDUI/oYNlEY64fk8ANn2zleM9hJB7+VJQ5dEDWqaS6juV78rlqVBwg1Bbq+xlkmaRji7hf9S5EDIIZ/3XoV3M646NVkVlSy7+XbuOvg0WoFBLnD4jk2jHx+NlluDoQ7Nr3OVRQRZCPprkCQyvcPCGRCwZFIUkSKqXEf2cPJsRXyzt/HqGsxsgr81Lql59/219ApcHErJSYJsc0IiGYEQlOmipJklCh2PqhKFHa/B7UFoukzbVL233585cNarg/WC3w/S2Qvxuu+BJ0/i2/yCdEZEPXLBTn5/h7xGTTYhIlh7ZSRJ39+4ocobdcUyRWeaY+0foBKRSiPnfwXPGzySCuRTlpYozcNOHaaF/GD+iBQZXEk+qt1IYPxXuM0ANPjWsoU9mVVc6cdzfy2Y0jmdhJpZENR4rZdLSUe6f1aXtHpVpk0IdcKVbAdAEi+PWLbPl8X/8qrHlClFG0VTLWBRRUGUjpEcjodkqIvk/L5smf0tnx+LQGGcgzAJcFxZIkBQE/A78AL0uSdI4sy0WuGt9pNN4w+PL6H4ur6igwriG+sS5kJzhcWM2/l+7hwenJzd3hIgYINQtDhTgZHGTcC7+TU65n1T0T6510Fi7bR9qJMpbdNd6hMf104ubhwUNjtmeWsuNEOTeO69kwAdv+Cfz6sLiIX7VYmEBU5YoypPITjbZMkcnZsxhkq9BSDYwTW8IECEvGHNKH898t5E6ffkzpbCYuOFFsrsQ3DM55zKYYIYFKw4YjhzmQX8XEPmGcPyCC/lH+vL29mpkXv4a0+Dp0n55P/fpKJ8omGjMuKRRJgp3qISRaTXBiU8tZspNYvC0bo8XKNaPjOJBfTVmtkTqzFZ25En68i8uLfma7bjTDrv9KZN3PQpQKiQP5VdwztQ9XjOzhsLRgYaWB6a+to3e4L7NTY5kxKLJJg1FLRPjrmryfJEk8NL0vob5anv45HW/NXv7f3CEAfLDuKOW1Ji4d2twxzu4U1tFgvEX6XSzKgf54VqykeAXCnu86JNWWFG4rm5Bl+PUhkXE+/3mhgNEW4/4FQ69tU5qsoNLA7uwKJieHibIRqwUkRedkE9W6hgmznboqyNsNuWkYMreizdiIVinjNeed+snz4cIqNh0t5aqRceSUi2a/KAccELccK+X13w5x97m9m2X/W0TjA30vaH+/MFupU9FBiHPMsdZVVNSaOuRsGtyoZMkTFLfMYOBeWZY32QLkVGClC8d3KZklQm0iPtQx8W2rLLP5WCm55QaGnaxdHm6ruyrcD3GjHT5Ge1NCQaWhPigurDI0FPo7gL+XmsqTpeQ8nPX8daCIN/44zE3jbe5ERQeFk1biZLGE6RchHrcHuy1hMYGhUmSLT7rRqQBf71WU1XazbuVGpQ8VehMK26qxJEncOL4n93+7i/XqMfS/eQt/rV/LlPAagkyFIgDoYNlEYzQqBVeNjMMnoS/sV4u64naCYotV5ovNmYxODCYp3I+kcD9Rz2esFcYOZcd5VXE9ub1uZJj32Wvfnhzpx98Pn9OxYKUNwv11vDh7MB9vOMazy/fz7PL93DopkUdm9Gv1Nd9sPUFSuG+zBMlN43sS7qetVx+xS7HdNKFlF7AFn28jt9zA8rsnOP4B4sbAxa9BVApEp4iVlj3fwq6vYOL9rb5s6/FSjhXVcGlqDOqNr4nG1zF3wZg72n9PSWpXq/evA0U8uGQ36x6cIuq+XaXOoPWDhHGQMA7dWKjMqSAi3AdJ3RDebDhSwn9+3Mf5AyLrg+JoByztvW29OHqTEyUuLWGv/y/KOOVBcY3RTKB3+z1L9qD4TJNlc9lfVZblvwAkSZoIjASectXY7sCeLXU0U9ym5q+9GaFgn1NBsf3SXlDZoH1YZXCiCQPqpeQ8eGiMwWxFq1I0LOdm28p/ZrzUEBC3h1LdZsf7ugendJmblSOU1xqbNLBePCSKF37N4KP1x5g9LJZ7d0bx453jCHLSUrheAWbnyA7pFSskeHH24GZqBtb1r6AoOYzlqiV8+72CK0Mcm+CfSTgbENuZPSyW2cNiySyp4bEf9vLTzlwent63xRIMWZZ5Ytk+rhkV33zVELh4SHT9fnPe2Sik2Pq03BeSFO7HXweL2lWvaBNJgmHzG34OToT4cTallPtazcx+ty2bVen5XK5eL0ohBs6BaU87dgwtEBHQoFXcw8H7bmtk5FeyL6eS2cNim8of2rDrJOdV6Mkt1xPorcbHgWuRl03ur9bo3H24GQFxoPISMpWnmE2PnIulA+ZgDUHxmaXN7OqaYgmYB5QBppOeWwAsAIiLayXT1IWE+2u5oAPLYq1hr79sZuABosNVG+B0s5392lVY1dCxW11ndkij2M7k5HACvTQN9YgePAB1JgtaVaPMTW6akEcLcZ3ObYs1y92ICr2JqICG7JFWpeSh6clo1Uq2HCvFW6NkQHQrdZWdpKbOjE/PifDnC+3WEUqSxNikhiyc0Wzlkme+4mfFaygGzkbZZyp/d7xfz0MniA/x4c2rUvHRKFu9XlbqzRhM1not3tbIrTBwoEBYnQ9PaPnvPSDaH5NF5mBBVYvBncOkXC2UHLI2t5qo2ZVdzlWhh5GWPS60tGe9Tf3SiQuIqtcq7qCZTgfJLdcz/yMxiZ8+MLLFYNd+XudVCEfKjpQHtIRdxk3vxGptiygUENZHZIpPMfa6+PYI8RErZSVnWKbYpaKksuBOYDdwyUnPvS/L8nBZloeHhXWNjWZbTOgdxttXD3N4Nu6jUaKQWskUS5JotitMd/j4DCYLJouYrRWelCl2Jiie1CeMu1uRCfJw9lJnyxTXk5Mmll5deFNcuiOb/7fq1GdCWqNCb6q3Qrdz+fAeXDIkmi3HSkmNC+qUTFdrvLL6IEOeXIUlYSIgCyfNVsgqreWpn9IpbKTvqlEpuF/xOVYZ0V3vwa0EeKnb/LsX2JIW4e3UMMcEerHh4XP49e4JTaTYGmOfdDmtV3wy/WeC2kcorrRATZ0ZdeFu7i5+WtS3zlvkUHlQW0S6ISiu0AsnwZo6M5/cOKLV7G/j9740NZb5YxMcer/G5RMuJ6zvKc8UF1YZuG/xrnq96rYI8dXw4pzBjOmkpnd3x2V3PEmSHpIk6Trbj4FA+7/VU4jByX9qSZIYHh/cukRLxACRKXbAMhTE8U1JDmNQTACjExuW5KYkhzn1T2g0WymoNGC0mTV48AC2oFhtuxyYjaLDu5PKCu2x5VgZX24+4dIxXcnXC0Zzx+Tm+qUnSmrJyK+ibwvato4Q5qfFbJUp9B8gApXF1wtb248vhKW3Cwe+HV/AsXX8vHYTizYewdL4OnJsHVOtG1nqMxcCYlmdXsCNn2ylvLvVa59BvLQyg7f+ONzic/YgL7IDjX3RgV70i2p9tSEhxAcfjdI5Z7uW0PrCgFmw7weheHISBzP28JH6Ray6QLj6O6caxFvDT6vCR6Mkz0VBscFkYcFn2zhWXMN71w2jb2Trv9cQHw1qpURehYFLhkRzeSM3yM4wtV8E6U+dT3KEG1xhw5KhMlv0ZZwiCirqWJKWTVFV+yURaqWCucN7kBjm2wVH1nW4snzifWCxJEk3A3uBVS4c26VkldYy9eW/uHZ0PI9d1N/hcRbf1obwfnh/qKuEiqzWG5PaINBb06KW8L8vdPx4QYj837YojeX/nED/lpaCD6+B0mM2HcXIBj3FlvRoLSbh4nNwJYy+XRgheDgteXLmgIaJYuE+sBhdHhSH+mooqzVitcrdUteyvvP+JGqMZgK81Fya2nbnfkeJDRJLtzmVFqKu/AqOr2tQ8jj2l9BAt0lM3Q4s0ChQfhTb0OSYtZlSdSRv113APIRT3h8HCtu0N/bgHOm5leSU67lzSvNyIrtxR0eC4vZQKCQev6i/ewKNlKtFXfH+n5paJ9eU0Gvl9VgxY7jiW3Qnuz26CEmS+Gj+CGKCXKOO8kdGIZuPlfLaFSmM7dV2k59CIfHr3RMI9tFyqKCKHsHezRzaOoJGpUDj2gX2BuwKFMWHIHZY2/u6iXK9mFh3pNEOxHlhlWXXlvqcYlzZaFcGTHPVeO6iQm/ihk+2olUpuHKUG2ubI2zONwXpDgXFdkwWK6U1RiL8dci2bFGHSh+sFnFy1RbbjBZiQKFsvRZaXw6/Pgi7v2l5PO8Q8ItqCJaVKmGuUGNT3dP6epZyT2P8dWr87TW/OWnia0yqS98j2EeDVYZyval9EfwupkJv4vu0bCYnh9PzJEWaflH+7HriPJe9V31QXK5neIrNca8xZiNUZrN+WxrL/trEXUM1xCmKG4Lm6kL+Tn6ezB0yBpOFnHI9EX46xxuzPLRLalwQfx4sokJvauYmeklKNCN7BhMd6HxQDHDFSDfdl+LHCpfG358V94OeE4SCyZdz8TcWUHnVd/jHteHY5gI6a5/dFjMGRbHiXxPazBA3Jincj/TcSi54fR1vX53KBYM6H/znVxj4YN1R5gyLbTPj7xBhjRQo3BAUV+hNPPfLfh69oB8BrQS95bUiLgjsoGPuo0v34KtVsejmU6uY4UrOqtSCyWLlzi/SOF5cw2c3jaSXk7PxR5fuQW+08Mq8lOZPhtvkewr2CpeuTrJ4axavrDnI+KRQftyZy4FnplNpMJP69GoWXtyfa8ckNOxsNooTKW9Xw1awF0yN9IgVKvCPJlXpx9caKz3XREOP3uIiqfUTS7ZVeTD5EeGIVVMIVQWN7CjzhPB6VZ4oCzFUCimpIVeI1+bu7PRn9NB9WLw1CySYO7yHaLLzChaGHC6kcbdydwuKc8r0PPlTOpH+umZBsauxS0Fll+lb3kGlgeBEXj2ST0nQRcReNklIUNiRZSKOl3GbfyFGi5WcMr3LAjIPLZMaH4QsC8e3SScZPmhVymbudM6gN1rYmVVOcqSfa88TSYLLPhCGHJ9eBKnXCVfG3DSY+xn+yU7IwHWQvTkVZORXMWdYc43mjrJoUyb9ovwZFh/U4YAYYP2hYl7//RCAw412VQYTH64/RkqPQNcHxYHxwq3TTc12S9Oy+WZbFv5eqlZXnMv1IihuLWg+mRAfjcvKYboLZ1VQvHDZPtYfLualOYPbXW7pCIWVBnLKW/mH0PmLDK2DzXa5FXryKgz0jvDFaLFSXmuixmjGYpXRKhWwe7EwTMjdKd7DYqsn1PhC5GAhyRM1RFhhVmQJw4XKHOTKEqSibLRVmbBjMxirxetCkuCm1Q0zVP8o6OhE+uBKSP9R1E97GvhcRmmNkSBvdZc0RX67PQuVQmELineK0gkXv2+IjxadWtEtJQErOnkzcAZvjYp/ntu7icvWyRjNVhJCfZiZEt281ESSGNkzmJE9Ra9BTrmeIU7KxHlomyE9AlFIkJZZ1iwoXrw1C6VCYrYTgV5jjhRVc+UHm3jjyqH1cm4uo8dIuH0j/Pm8sDuXrVSd+1/u3xbFHb7lbv8/Wr4nj/fXHuXSoTEOyef9vDuXx37Yy6yUaIbFd875bd2hIrYcKwUc0ygG8NK4SX0CxOpraG+3NduN7iWy9FmlrUzGAYvFip9W1Ww1pDWCfTTsc3VT6CnmrAqKp/WPINJf53CR/cn469RkGKpa3yG8UbNdXaWYlVcXiMyr/fvqQpGVHTyvwcoSKKoS2bSYQCEZV9BIlm3wiY9h78ugCxSB76jbxNeoFKFJ2YZigL66jnnPrOHJMQO4fkw81JYIW96wZMedsKKHCp/3suPCRteD0xRV1THi2TVM6x/BB9cNb/8FTlJntuLjoxLLqYX7IXmGy99jXFIIGU+7flxXUB8Ud/Bm4Czt2cRqVAr+7/Ihbe5TXWfGZLYSE+hFf1dnrTw0wVerYlKfsBZLVL7YnEmAt8ZlQXHvCF8UkrCadgsabzjvaRh0OZQdZ6M8kpW/bGfBRBe7RrZAVIAOs1WmpLquXbWOk9l8tIR7v9nF8PggXpg9uNPv3VgyL9TXsQy8dyOdYrcQlgzZ29wydN9IfyYnh3G0uLrVfeaP68n8cR2/hwf7aiitMZ5REq9nVVA8OTmcyckti6Y7QrtGGBED4OCv8GwkmFvIKCvUoonNaoITm4V7mK84vqKqOsJ8tYT7C1mcgso6fDRKpih2kLz3FSGsPvt/nc7m+XupeXhGXzHLtrsQteNE1C7RtvKR3B2eoNhF5Npcl1anF/B9WjaXpbrmhtsadSYrOpUS8neDbIFo19YTQwdr4U8RFbYGk64KimuNZgoq61os1agymMgsqW2zeUWWZUY+u4Z5I3rw1QLHDYI8dJyWGp9BmFH0caEagValJCrAixOlte3v3ElkWUaWReMZUYMhajC7VmagVEgMiHZ/s1SkTS84v9LQqaD4YEEVt3y2jR7BXvzv+uEONck1tnV29FrUIMnmJvWmsL6w93uhEKJxbRlXUVUdvcN92X68jFqj2SWNuSE+GowWq80/oXvr0HeUsyoodjV+OjVVBlPrs6SUq0QWWBcogl/fCBH02r962QLT4kPw9mjhVX/xawAUVdcR4acmWipFwkphpYE4azavqd9EHzIA70vecGh5W61UcNuk5rJTThHeXwT4eTth4GWuHfssZUiPQI48dwFXfrCJx37Yy9C4ILfWuhotNkm23B3iARcrT4C4Id+3eBeTksOYmeIaJQdXYc8UB3p3Ta3zy6sOsmhzJvufmt7s2vF9Wg5PLNvHyn9NJLkVGThJkogL9ibLDYGTh7ZprJ5itlgpqqpr17ijs8SHeJPphr/tP7/eybHian7+R0P98M6scvpG+jkUaHYWe2CaV2FgcCfm+Z9vzESrVvLJDSMdPkftAfnc4Y4nGLQqBQoJ90mahiUDsogJolvoVXKChcv2seNEGTv+M61V3e1XVh+kzmzl4Rl9OzTm9AFR9I30P6OafM+cT3IK6B3hy4TeYdS1doKE9IJL3hBLVWPvgsGXi07z8L7gHdwQ1Ib2hpELIO0zyN8DwOQEH56vfYKYj4ex3/cOZuy6i6HrF6BQ66ie9alYAnOQrNLaev93l6DSCrMST7OdS8gqrUVvtKBUSLx2RQoalYL31x5163sKRzuFUJ7wixI15S5GkiRW7y8gLbPM5WM7yzWj4/nrgcn4aNwfGADEBHlhMAllmcbIssyiTZkMjg1oNSC2ExfszZr9hUx/dW39yoIH91FSXce4F37n661ZDY/VGLHKEOECObbGuGvC0zvcl705lfVjW60yu7MqSOmimnT776mgsnPNWQsvGcD3t491yh7aHpA7UzctSRKHn72Au6e6SX60XoHC9XXFRdV1xAZ7t2lEs+FIMbs6YNxhJy7Em4l9wpq6oZ7meIJiJ5iZEsOnN450zQx70oMio7ziEdCXc3feg8SUbYXx96IbPAvfukI0deX4Xvsl4T2cs96d//EWnvtlv/PH3JioFKF64aBZiYcGHvl+D5e+/TeyLBMV4MXiW8fw9MwBbn3P3++fzJOXDBSd6G4onbAT4qPplrag3hoV8SE+XVbiYe9+P3lyuvlYKYcKq7lmdPvKH3G2ACEjv6rLyj7OZoJ9NOhNFrY3mtTZTQ5cHRTfOL4n713r+l6C2cNikSRYkpYNQFmtkV7hvq3aTruaEB8Nq+6Z2GH1iY1HSsgsqUGpkJwKiAHCfLXs+s95XOWk5J1bNdaDE4VSlBsUKIqrRUnmB2uP8sj3e1rcp7zW1GGNYhB9DSv25pFdduasWHmC4u6CVxBMeRSOr0N+ZyxyThpc/glMfYL8SS9x5PLVGO4/jinW+fpBP52aypN1ip0lOgUM5aLZzoPDHCmqZv3hYi4aHFUfoPWJ8EOlVFBrNDvtxNgaOrUSL2s1lByGGNeXTtgJ9tE0y452B37cmcM3W7vObc9uYHCyLNuiTZkEeKm5eHD7qgPxIQ1BQmv2th5chyRJpMYFkXaiISgeGBNAxtPTmylSOEufCL9Oqyu0x4bDxby0IoPkCD+WpGVjtcqE+Gr54c5xXDrUvT0LdhQKiT4Rfh2qZzVZrNz/7S4e+Ha3y947wAVqPi+tzGDRpkyXHFMzlGqhBOWGTHFxVR2hvhpyyvUs3ZGNydJ8hbtc37mguKzGyG2L0thwuMSVh3pK8QTFTrDteCljn/+tQz7hHWLYDRDWD7m2hBvr7mW5RTR2PPDdLu5bvIs3fz9M38dX1Jt4OEq7DYKOEGWrf8rzlFA4w6JNmaiVEvNGNM1mZJXWMurZ3/hxZ45b3vepn9LZuflP8YMb6onthPhqu2VQ/N327CbL4u4m1qYqk9MoKDaYLGw4UsKcYbH10k9tMcYmsdQj2DUOYR7aZ0yvEI4V13CsuMEqWadWurymstZoZumObA4Xuk6BYtPREpbtyuX6sQlklerZcrzU6XuJI6zYm89XW9qfgC7dkUNOuZ7bW7BeP5WsTi9g/aFi971BWLIoozS77jpZZ7ZQaTAT6qtleEIQBpO1mZSaLMtU1JoI8Op4zXaITcWj9AyymPcExU6gUEjkVhgoc9U/hFIF1y9j50W/8odlSP2SaJiflqKqOqrrzPhqVU7PdP293JApjhggmu08dcUOU2s08932bGYMjCLMT9vkudggL6ICdSza5Ppsptli5aO/j1FzbIt4wI3lEz2CvLtlVrMlpzJ34u+l4umZA5jQp0H5RadWsu7BKdzVgpVwSySF+9Enwpd+nTAw8OAcMwZGAkJvF2DZrlye+dkxLfq2MFlk7vlmF7/tL3TZmHtyKugd7seslBguHByFt0bJvPc2sXDZPpe9R0f4eXduuz0SFqvMO38eYUC0kBHrTnipldS6acUOgH6XQMUJ+OoKoULhAmQZnp41kMnJ4QyPF/rm20/q7agzW+kR7NUpIyBvjQqdWtEtEx2O4gmKncBfJ27ulXoXBpi+4WRL4sJrD4wi/HUUVhmo1JvwdUFA4e+OTLFKK1z8PJlih/kjo4gqg5nrxjSvJ5UkiWtHx7Mnp6JTjRAdwWhbRousyRAOh97BLh2/Mf+5uD9Lbh/rtvEdpbzW1GFrU1cgSRLXjkmod+SyWmVkWcZHqyKoEy5mQ3sEudQ610PbRAd6cc/UPoxIEOfI2oNF/Lw7z+XvE+ClJsBL7VJZtr25lQyI8cdLo+Stq1LpE+FH2omyDq1KuJJIfx35FYY2s9S/7MnjWHENd01J6nZSjl4aJQZ3mHfYGTQHLn4djv4Bn80CvS14tZig9Bgc/QvSPoc/X4DN78ORP6Ayt81+Hp1S4lrvzQz66xYiKSEm0IvtmaVN91Er+e2+yVzX2C23JWQZTIb6gD3YW0NJ9ZkTFHe/lM1phL9Nl8/VAaa9eSPM1xYU+2kxWWROlNbip3P+TzY7NZbR7riRRqdA+jKPs52DXDg4ioTQ8a0aMcwaGsPzv2bw+aZMlzpP1dk0NyOq9kGvs1PztqszxSCa7PLK9QxPCGbN/gJeWnmAj+aP6FRD0X/ndN7EwINzNFYeKKg0EOFiOTY78SHeLguKCysNFFXVMbCRFvHKffmYrTJDYrvWDTEyQIfeZKFSb27VQTK7rJb+Uf6cPyCyS4+tI3hrVBRWudnaeNj14BUIS26Gt8eApISqXJDbkILT+Aolq9Bk29c+YqspxLzycVT5toTVuv9j+sBbWqwpBmDnV8KMy6TMOgHmAAAgAElEQVQHcx2Y9SIINjfa7Fy1mGBfb0pr6lz32U8xnqDYCfzaCIp3Z5czKCbAoVluUVUdaqVUX/BuFzk/UlRNUrivE0csGJ4QjFs80qKHClk5j7Odw7QloO+nUzNraAxLtmfzn4v7469TI8sypTVGKvQmKg1mEkK8O63jWWe2EkwlvoY8t5ZOAGw9Xsorqw/y39mDne4mdxVWq0ylwURAF2kU23nvryP8sCOH3QvPZ9HmE1QZzE0MBjx0XzLyK6nUmymoNJAQ4h798Lhgb3ZnV7hkrOJqI8kRfgyOFdcXWZa5+2sRJA2N6/qgGCCvUt9qUHzH5CQWTEh0r9KDgwR6qSnvihra/jNBFwAb3hQGW4FxTTf/GOFIW3xQNOYVHxLfH18Hu79uMpRRF8EDxtt5emgFvjsW8fjdDzaT3dyZVc6Hy37jtZJ/ogiKE0oYKp3Y1LpG33uJleG/X4P0H3lpzgv1piZnAp6g2Al0agUzBkaS0KgL/I4vtqNTKflxVy5zh8fyzKxBnfZ4HxYfhFKRWB9QD40L5NV5KRRUGjrVGdoapTVGjhRVMygmwLWC7Y2b7YJ7gqFCzDS9Q0Bx5pw07uDhJbvx0ap4/KL+be5328ReXDkijto6C6+vOcSq9IIm2aR3rxnG9IGdy64YzVYGK201fjHuDYprjaKZrKDS0G2CYoVCYv9T07F2cdNRTKAXlQYze7IrWHuwiHum9mlTQ9RD9+GhJXuwWK3kVxjcs+qGyBT/ujcfs8Xq9P9F/2h/Vt4zsf5nSZJ4ac5gVu4rcLmcXHvYJ375FYb68qHGGM1WNCpFtz0XXp7nWlONNkmcLLbW8IsUW8+JTR+vq7IFyYfAUsfnpUNZuuYEL0zqC+nfwMY34fxnmxjR5JXruST/LWSdGq7/uX2t+rzdcOR3+s30O6NWhj1BsRNIksQ71wyr/9lilVmzv5AbxiZwx+RevPH7YUprjCyYmEhKj6AOB8fT+kcwrX9E/c9RAV7MGuo6B7A/DxRy7+Jd/HH/ZNe6pNmb7ZY/CD/9S0i0ASCJwDg4Ubj8DZoDWtfZop7uFFXVsSQtu2PatLYJWEGlgc83ZTKmVwjXjYknzE+Lv07dpjVwW2N+PE2J/KeEFDWk06/vDCG2ellXaRVX1JpIyypjipP27V3h5nUydlm2F1dmoFJIXDGyR5cfgwfHuHBQJM8tF1qyrnazszN/bE+uGR3f6aRKR7l8eA8uH971/3ODYgLZvfA8/Frpj7njizT0JjNf3Hx2lnK5BK2fSHDYkhz5P+3DV6tCG94LBs1B3vYRF6aNYPLQvjw4XRiGeJ34g8nK7VSOegz/jpg3JZ0L6T9waO9WNlWHc217tcinCZ6g2IXklusxmq0khvkwb0QcfjoVL644wMp9BYzsGcziW8cAQn9R3cYsuKS6jgAvdZOZ8vbMUvRGK8Pig5xujGgo+3CxAoVKC6Nvh4J9EBQvmrZUXlBTJLasLfDzv2DVYzB4Lky4HwK6l93vqeCbrScwWeQOBcV2Ivx17HrivGbB3PHiGr7ZeoLbJyd16mYq5e4Q9WdunqwE24JiZ7uVT5TU8sXmTML8tDzzy37WPzSF2CDHMs8nSmr5eMMxrh0dT2KY8+VJHcV+vOsOFXPBoMguz9h5cJwZA6N4bnkGD8/oy60TE93yHicr0DjD9FfXcvGQaO7soLKJO9GoFG1K2O3JKWdsr9BWnz/VLNuVy8p9+bx1lXtX1VxJcbWRUJt8GuPvRdr9DVdqfmFDsW1SZDaSsvcFjlojiRp7Z8cG7XUOAIU7fuHx9JFcPrzHKUkuuBpPUOwkV32wCV+tivevG86RomoAeoaKG+uCib2YNyKOtQeL6lcX6swWxj7/O0N6BDK1XwTn9gtvdjM8/9V1TO0XzguzG5pobv50G2W1Jm6dlMgjM/o5dcwNqhkuVqAAYWndGrIM2Vth28ew4wvY9TWMvwfG/kPUKZ2M1QqmWtHlaqoRX401YKxu9P3Jm+05U23z/aKGwJyPRPDeTTBbrHy5+QTjk0Lp1cmArKUL0J6cCv5v1UFG9gxhZM+OqUgcLawi7OgW5F7n4G5xL3tQXFLteGNGWY2R+Z9sobTGyEtzRGZ789FSYoc5FhQfL6nh47+Pc+GgKBK7UP3J7mo3PD6I2yZ1Ly1WD23TI9ibIbEBLN+T57a/ncFk4cP1xxgWH+RUiUZxdR0Z+VXMTu0+5Qhv/3mYEB9NMz32wkoDBZV1DHJgxaurOFpUzS+783j9CtltWXxXI4w7bPe98L7Q72JmZ/xCWv5wOFYLB1cQqM/kAfkh3vfqoO55QCyEJpNYuQUYSUmNsf6adjrjCYqdRJap1yk+WiQkShLDGkoSArzUXDykwZ1Kb7QwMyWG1fvz+T2jEJbCkNgAHpzel3FJoVisMqU1dYSflCUI8tZQVmuqV7xwhthgb1QKiXsW7+T/Lh/icjemVpEk6DFSbJMfglWPwx/PiiA5sEfzANfUSY1GjS9ofEDt3fC9LhD8o0FSQPqP8OtDcPGr7vl8DvBbRiG5FQaeuMQ1Ns5T+oajUSpYsTe/w0FxSd5xEs2lZAYMcHtQrFMrGRwb4LC0oMFkYcHn28gu0/PFzaMYFhdEoLeaTUdLmN1B69iTqbBJKna1+kSor4b3rh3GoJgAos+Am8nZRmp8EB//fZyiqjqXZnXtqJUKXltziBvGJTgVFO/NEc16jpRWuYtf9+QT4ts8KN5jO9ZBsd3nWE/G3lRmMFm6peZ6S/zz3N5YrI16Jibcj/f+n3il+kH4VDx0PHQyet25nRMHSDqX8C0fosVImSco9gDCHS6zRDQ6+WpVjOwZXF832RKB3hr+c3F/Hr+oHwcLqlmzv4ANR4rrJQZLauqwys2Xzry1yvr3cJaYQC8W3zaGF5ZnnLpu96AEmPc5HFsnulgtRvAOFYFsi5uvLdj1aQh4G28qL1C0kwlZ/QT8/aqos0q9rks+Znv0jfTjzim9OLevczWxdny1Kib0DmXlvnwev6hfhy5w2iLRhW6M6JoGkmV3jXfodVarzH3f7mLr8TLevGpovVbsqJ7BbD5W2s6rW6f8FAXFkiR1S8kpDx3j4Rl9GZ8U6paAGECpkIgN9nJals3uXNY/uvuYvEQG6Mhq4XPtzq5AIdGqLGV3wMtmUV1rPH2CYrv7ZT3RKawc+THfrN/HS1eNISQomITIwSxSdvLz9DoH5aa3GaXYT0mNY9f17sbp8Rftxvh7qetrc+eO6MHcER1rXJAkieRIP5Ij/ZrUedVrFPs1DVa91eJP5QqdYoDUuCAW3yZqnLNKa1EpJaICTsEsr+cEsXUF5/4H8nbBL/dB+ACIHdb+a9xMfIgPD5zf16Vjnj8wkt8yCtmXW9mh7JBP0W5MshJrhGuy1R2hps7MvtzKDmezAQ4XVfP7/kIevaAvFw1uWH0ZnRjCyn0F5JTrHcpU2M13/Ls4KPZweqNVKTm3X0T7OzpBfLB3fdLFUfbmVBAf4t3lk762iArQsaWFieyIhGDuPrdPtw42vWxla3p3Gni4ELPFyrpDxfSP9m9Sqhmbci5x5mRMPXqBo8mx+HFYlVomKnafMVrF3afI6DTFz0XucCXVdby/9giFlfaguGn2wd5c561x/cVi9jsbeG3NIZeP2+1QKEVNsV8kfHM15KSd0sNZsj2bzUdLXD7u1H4R+GpVHCqs6tD+fqV7OSjHotF1XZPZwmX7mP/xFg4XVre7r8FkodJgok+EH6vvncgtE5o2Nl08JJrV90wk2sELu95owVujPCOaRDycWcQFCwOPttzf2iM1LojLHSwtchcR/joq9KZmgeX43qFNzFG6I0HeamKDvLA0+pscL64h3ZaR726U1Bi54ZOtrE4vaPL4gOgAFl4yoF495aZPtvLc8v2dG1zjDXFjuC78KDMGdkCx4jTAExQ7yciEYC5NjaGmzszQp1bx7bYsh8b5+0gJzy3PICO/igenJzfRPga4Y3IvpvaLoG+k69UBgn00Z5R3eZt4B8OVXwuHoA/Pg03vtGmP6S5qjWYW/rSPL7eccG4gixnKMps8FOyjYfvjU7l0aAduhLJMYPle9ktJaNvoCHc1952XjE6t5K4v0zCYGm6MZouVb7dl8fyv+7npk61MeukP+v9nBe/9dQQQig0nl4SE+mrpHeHnsB3s/ecns2fh+Y5/GA8e3ERciA8Gk6W+7r2jGEwWlmzPRpZlbpmYyF3ndK9AMypAh69WRXGjhtvqOjMH8qswt+a01k04t18E6x86p4mc6eT/+5MLXl93Co+qdeyrz/WNdo0wmq319/70vErKHIgDFEnnoCk9gKo617kD7SZ03zWK04QZg6KYMSiKvTkVlNWaHF72uXBQFC+vOsDyPXksu2tcsxv8qMQQRrlJJF408Z0lQTEIPeXb1sEPd8CKh+HQKvAJE8FlZa4InEOSIKQXJM8QTn0u5ocduVQZzFzbCRm2ZlhM8M21cPBXGDQXpi6sl7jTqkTWs7E4e4uUHkVjqmTOJZdAFzZJRAboeHnuEOZ/vJULX1/HOX3D+feF/VEqJJ78KR2j2UrPUB8GRgcwKyWG8b3blmjaeKSEPw4U8ugFjimznC5d5B7OLq4eFcf1Y+I7ZWRRXWfmsrf/5mBBNT3DfEiNC3LjETrGrJQYLkttOmnfeKSEWz7bxpLbxzAsvuNlVd2JilpTqy59pwr7xCPMr3mv08y3/iYmUMf/rh9Bea3JMXOwXufC6v/w99u3E917KL2jgyFpKkQOcvbQTwmeoNgFmC3W+mXgxsoTnUGpkLhtUi8e/n4Pi7dlNevKdSfBPhoy8rvn0o/b8A6GK7+CTW/Dn/8VdpqBcRA3GmqLhXTc3iXw139hwGVw7uPCfMQFyLLMZxuP0y/Kn2Hxbdyw9n4PpUdBqREycjHDINZm0G21wPe3iIC4/0yhrJHxs5C3ixtNnXcUV3+bw5RBCW1rk57YJL66IfBvj8nJ4Tw0vS8r9uXjqxUXY0mSWHXPRML9tJ0KBNLzKnl/7VHmj03otJLDa2sO4aNVcvME9+jNevDgKI6U9CzemsXBgmrevSa1WwbEQIsT9T3Z5bYmu+6rPAGiVOLxH/dy97m9GZ7QNHjfl1fR7TSWi6tFwqulTHF8sDeHCqswmCzoTRYCHbG6jxiANWooE/LWo0hfB+nA+lfgjk1C+ek0wxMUO8mKvfnctmg70wdEIkmQEOK4Q9ylqTE8/P0eHlqyp0uD4iAfNWW1LjbyOB2QJBhzp9hawlABG96AjW/B/p9g+I0w6UHhQ+8Em46WkpFfxfOXDWp9yf/ACvjuhuaPJ0wQ2s57voN9S+G8Z0QgXHYcVv9HBPGAFvgOqFzrBxkJIoMcEAv+tq8VWZC+DPJ2YtCG8sDvdbx6Zdfrbt4+uRe3T26q8+qIPNnoRHFz2nyspGNlI434dW8ePYK9PUGxh26HLMs8sWwfw+KDmJnSvtGRxSrz8QahbTy9G9d4Wqwy9y7eyTl9w+s/156cCnqH+zltTuVuTLbGtbmN3AB3PXEeRVV1JIV3XV9GR7FnilsKinuG+fBbRkG9w6hDmWJJQnHrnxRX1zH7zbVEmLP52vwIih/vgmuWnHYW0J6g2EnsEmm7ssuJCfRyqllHq1Ky+NYxVNd1bYB6WWosI3u6pzTjtEYXAOc8BiNuhj+fh60fwM4vYfzdMPoOIQXXAUwWK2W1RswWmehALyIDdAT7aJiZ0sos2lgLvz4Aocmw4A+QrWDSw55vRZC+6DKx3+RHRUAMQuJu7mdQkW0rA8lh885dHDyYwRxvCa+KbJEVrrfeBmKGw7Sn+bQ0hV82FPH66XXtakK/SH8CvNRsOlLa6aC4Qm9iUDfqzPfgwY4kSfy2v5BV+wqorjMzOzW2zXvMmv0FZJXqnTZ4cjdKhcQfGYUEeKmZmRKDLMvsyalgspN27V2BPWhv3CQY4KXuVuoejblkSDR9I/1aLO3sGeKDySKTXVrLlOQwp5J6ob5a/nfDaC57ewPv+c7n9iPvwLaPYMRNzhx+l+OyoFiSpADga0AJ1ADzZFk+4wtV7RJpSeG+DHXBUlVnJKpcRWpcULddZusW+EXCxa+JQHjNk/D7M7DlfzDlEUi5BlrQdswuq+W+xbtIz6usVyc5f0AE7107nJ6hPvx694TWlUTWvgTlJ2D+Lw2Bt9ZPZLRH3Ay7vxH1xMNvbP7agFixAeHRFzIv/U+Mvfpz0/ie4vm6aqjMEePZlrZKlu9Hq6p1uFGtO6BQSIzsGcxfB4sorq5rMSvSGhV6U7e9oXnw8Mq8FJ79JZ1/L93Lq2sOcfP4ntwyIbHFEoSYQC+uHNmD8/q7VyrOFUQFeJFXYQAgr8JAcbWxWzvZ2amXZLM1CGeV1vLh+mNEB+rIKtXz9KyBp/LwmhEd6NXq6luCrVlQb7Lw8Q0jnX6v3hF+vHT5EN78TceNIfvQrnoMEieL/pzTBFe2m18NvCzL8nlAPjDdhWN3W+xB8WWpMdw7rc8pPhrHqNCb2HKstF5v2UMrhCXDlV/CDStE/fFPd8M7Y2H3YshYLmyr0z4DYw2P/7CX9LxKZqfGcs/UPjw1c0ATKbGTrb3rKTogssFDroSEFsTQVVphPDLipnaXpXqG+tA30o+Ve/MbHtT6is/RqNarzmRBqz79hWiuHhWHRqXAxzbZaKxq0RpGs5Vao8WxZUMPHrqAkT2D+eHOcXx58yiSI/xYd6i4PiA+WdJsYEwAz182uFP1+KeKyAAdfx8u5rbPt1NrtPDx/BFMPQ2Cee9G5h0Ax4pr+GTDcbYcK+XzTZkOKTi4k98zClrUhAZIjvDjkRl9myhpOMv0gZH8+I+JaGe/Awo1fHcjVBW0/8JugssyxbIsv93oxzCg0FVjd2fsgv922ZPTkV1Z5Vz30Ra+u21Ms8YBDy0QPwZuWiUa29YsFA1vjTHpef6y66muM3euxkyWhbGIxgemPe2SQ719ci/qzG1LHBlM1i6VY3MXk5PD+f2+UFRKBWaLlQteX8cD5yUzY1DrtZU1dWbC/LSEdCKz7MFDVyNJEmOTQhmbFFo/2cst13P+q2uZnRrLLRMT2XSkhJS4QHqFdb+61pb457m9WbQpk7QTZYT5abtlPW5LaFUK+kT44u8lwif7vf+cvhGs2V/InpwKJvYJO5WH2IQXVxygR7B3i6vQAd5qbp3Ui6U7srni/U38cOe41hM2nUCpkKjQRLAy9lEuz3wC6d3xMPsDkTXu5ri8pliSpDFAkCzLm056fAGwACAuruuayNyNv07NwBh/nluewZDYQLfJprmTYJst9VmjVewKJAn6XQx9pkPOdlBq2FUsE7vsCoKP/03kqFs7P+bub+D4OrjoFfB1zUW1I805vjrVGeFZD9RnyIwWK8HeGv759Q7eUyv+f3t3Hh9ldS9+/HMmySSZ7CshZCEhsq8h7FBWC6hFrRa8aq+oWC9Xb13rLbVuba21Fv31WvXqRVsqahXlXlBR0SqCLAJBAmFTiCRswZCQheyZOb8/ZjIGsk1gZp6Z5Pt+vebli8kzz5znPM7M9znPOd8vMwa2PQIVE2Zm+4OzvNlEIS5KyznFs4cksWJrISu2FqKBn45P59F53qtMeTFGp8d0nH3HR5lMinX3THX+u8SxkG36QPt3tq8FxafP1nc4tbO4oo7PDpRwsqLOrZUEvz5VxX/uS+XksJe4q+xx+PtV9oXqU//TXkjLR7l1eEgpFQs8C7Sa7Ki1fklrnaO1zklI8J3/YS6WOdDEdY5MEamxlk629k0xjqDY3bmKG5psLt3C9msBQZA2HlvvUTy8sZZtehAUbe56QZDaM/DRg/bFb9kL3drE7yrr+GRf+7evHrpiMKv+fZJb39NoFnMgr9w8hkG9I/m3FTvZdOi00U0Swq2So0P5009GsOGB6fx0QjqZ8WHfrx0QXlNSVY/FHEDvqFD6xlnYfay88xd5idWmKatuICG8/VRrj6/dz5q8EwSaFGFuzPwxpm8s80en8tw+M7ZFn9qnBH7xDJz+2m3v4QluC4qVUmZgJbBEa13Y2fbdyd4TFQAkueG2gxFiHPMpy6rdN6dYa82cP29g/otb3LZPX/bu7hPkHaug17DpqOoSKD3ctR388zdQWwZXPA0m905lWPbFtyx+LbfLVbH8XWRIEMtvHktGXBiLlu8gt/BMq21yC8+waPkOikprDGihEBcvOTqUR340hI/vneq3AzP+ZvGKXJauOwjYq5MmRtinX41Ki6HR6vkKqTabZtHy7dzz5i42HzqNzdb2e5ZVN2DTEB/R/vSw5vnEoeYAty+2HpoSRUOTje/qA+HqF2DxZkj07cwo7vz1vRXIBh5USq1XSi1w47592hvb7KWdO6wc5sNCgwIIDjS5daRYKUWAUuw+VtHtR4vrGq388cODDEmOZOSky+1PFm5yfQfHcmHHX2Hs7dB7hNvbN3tIEo1WzWcH2p7m/8Ta/Tz+/j63v68viAkzs2LROCZlxbc5RaSwtJpP9p/CakCpbyGEfzpcctZZsOuJHw/nn/dNA+Dp+SN4ZeEYj7//N9+d5ZP937Em7wT3rcyj+durur7pnO06ylHcLCPefiHVnCXJndIdF2lFZY5Bh3jfKjfeFncutHsBeMFd+/Mnc4cmOacg+COlFM/fkE16nHtHGe6fPYDbX81l38nKbp3ybfnmIxwvr+WpnwzHlBBnLxlduBlG39T5i61N8N7d9rRv03/lkfaNSo0mMSKYD/OLuWpU6znGO4vOEOQHq9UvVEJEMMtuslcCtNo0J8prnSNq5Y6iNdGSkk0I4aLQoABnSjb4vky8t9Jabj9izyax9udTsGl70aUmq40ZS9eTlRjO/JxUZg9JIjMhjA/vnkLvyPbXjGTE2xc49u/l/oWOfePCyEwIo77JfwbGpHiHG7xw42ijm3DRZg66uFQ4xRV1mEyQGBHCklV7SI+zcJVjkVfe0fJuHRT/ODsFizng+/KeaRPsQbErti+D4t1w7V8hJNIj7TOZFLOHJLEy9yi1DdZWFaPqm2zOIjTd3ePv72f1ruO8eft4shIjnFNKIiUoFkK4KNQc4EzJ1lyZ74rhyTRZbfz05W3MHJTo0QqZzRk7+vcKdwbiDVYb149NZ2XuUe76xy4iQgKZNyKZWyZnENVByskMR8GOa0d3reiRK9LiLHzqGEX3F913eEh0Sf7xCjZ+U3LBr394dT4zl37Ob9/bxxvbijhb10RSVAiJEcHsPlbhxpb6noSIYH46oe/3T6RPgooiKD/a8QsrT9oLgfSbCUOu9mgb5wxNoq7RxldFrefV1jfaCA703dXA7nTj+DSUUtyw7EsKS6upqG0kIiTQ6+WthRD+KzQogNoGK/VNVlbtPE5BSTVgz35zqrKu3bzA7vLEj4fxj5+NP2dk2mIO5K5Zl7DhF9N5/bZxzBrUi7dzj3GyvK7DfUVZgnj+hmzm+nBZcG+SoFgA8OKGAh76v/wuv27VzmNc8exGbp+aybA+Ubz8xbekx1m4c0YWAK8sHMNDVwx2d3N9gs2muX9lHlsLSs/9Q/pE+3+LOllk+NGvwNoAlz3l8frw4zJi2bpkJhOz4lv9rb6pexTvcEVmQjivLRpHQ5ON6//nSyrrGhmYFGF0s4QQfmRwciT9e0VQeta+DiehxUK2YSlRHh8ICg4MaDcftcmkmNgvnmcWjGT7r2cxsV/naWIvG9bbY4s0f792P4tX5Hpk357QM34JRafiwswXlKd497EKCkqqGZUaw2uLxrHsX3N4ZeEYZy7NoX2inHmQu5ut35bar8Qras/9Q68hEBzV8WK7w5/C3lUw5T6vlMAMDDCRFNV2dpTUWEu3yVPsigFJEbx66zgq6xo5cLKKN382wegmCSH8yC9mD2Tp/BHOwh0JLRayZafFUFxZx5HT1R55702HTvP7tftdqkAbGRJkeAKAytpGth9pfYfSV0lQLACIsZiprGuiydpx9bPzHSyuon+vCEwmhVKKWYN7nXMFe6a6gb98+g37TlS6u8mGe2v7USJCAlvfdjIFQNq49ucVN9bZK9fF9oPJd3u+oQ4nymu5+a/bWuXsffXWcTwwZ6DX2uELhvaJ4m83j+XReUMM/9EQQvgnZ1DcYqR4+oBEAD5tJ9vPxVq3t5gVWwsJDfKPKW+psRZOn61vlRnDV0lQLACIDbNPxC/vQi5brTUHiis7vP2sFPxp3des/7p7Vf2uqGnkg/xirhrZ55wKU07pE+1Jys+eN0/bZrNPmygrgMuXQqD3ygvHhpn58tsy3t9z0mvv6ctGp8e0WfpUCCE6smxjAbOf2UCj1UZCRPA5QXFanIVrslPavTN3sXYUnmFUWrSzeqevS3NMyzh6xj9ywftHrwqPc1a168IUipKqes7UNDKgg6A42mImPc7C7qPda7Hd6rzj1DfZWDAmte0N0h0V4opajBZbm2DNnbDjZZhwJ/Sb7vmGthASFMD0AYms23sKa4tk79e8sJl/bCvyaluEEMJfVdU1cfBUFbOHJLH9wVkknzf9bOn8EVw2rGsL146W1fDi54eZ95cvuHHZl+g2cqdX1TWy/2QlOen+czHfnOrVXwok9Yw8TKJTE/vF887iiaTEuD7Zvr7JxuXDe3dYVx1geEo0uUc8uxrX2yJCArlsWBJD+0S1vUHvkfZ5xavvhG83wMjr7SUu978L05bY678bYPbQJN7fc5KdRWcY0zeWJquN3MIzTO3ffUqvCyGEJ1kcaS1rG62EtZPOsqK2kZqGJnpHdbxeo7C0modW72XD1/a7iv17hfPovCFt5jz+qqgcm4acvv6T4jQ9NozJWfFYzP4RbvpHK4XHxYaZu7wgLjXWwnPXZ3e63YiUKN7NO8F3VXUkRoRQWFpNcnSoXxeMuHpUCleP6iCvY6AZFr4LW56Hna/a8xEDzPkDjF/snUa2YfqABMwBJj7ML2ZM31gaHASn0zgAAA93SURBVHPIgwP991wIIYQ3Ned6f+zdvVjMgTw6b8g5f7fZNNP/tJ5ZgxL547UdVyl95uOv+arwDL+YPYB5I5KdWSC01vzXPw/xwyG9GNTbnsO+rLqB+PDgTgeifEmUJYgVi8YZ3QyXSVAsAHtarvfyTjI4OdL5AexMW4Ug2jIiNZrgQBNHTtegUCx4cSsT+sXxzIKRF9tsQ+w+Vk7/XhFtzyVuqfcI+PGLMOcJyH/HXrVu0I+808h2RIQEccvkDJIi7XPg6hslKBZCiK5oXuS2/mAJWYmtU6OZTIoJ/eL47GAJNptutZg3/3gFFnMAmQnh/PqKwfxy7qBWc5DLqht4Y1sRy74o4O5Z/blxfBpXjerDlSOTvVY5z5201n7RbvklFABoDfetzOvSitlr/3szd7y2s9PtstNiyH9sNiNTo7njtZ2U1zawaEoGRaU1F5QGrqvcWWKyrtHKDcu+5OHVXcjpbImFsbcZHhA3++XcgSyclAHYp8AABPvJSmYhhDBaaqyFmQMTOVPTcM4iu5ZmDEikpKqevS0yL9U2WHli7X6ufG4Tf/zwIADx4cFtLsqLCw9m5b9NYHhKFL99bx+znv6cD/ac9IvA8nyPrM5n7p83Gt0Ml8hIsQDsi7DCzAEuB6lNVhvffHeWCZmdJwYPMCkCUDyyOp9tR8r483UjSYoMYcIfPmXhxL786rJBF9t8py2HS9lzvJyCkmoOl5yloKSavvFhvLPYXlCjrav2rvgwv5iquiZnCWt/VdPQRGOTRinIToumV6T3smAIIYQ/G58Zx7iMWAY//NE5OYpbmjYgAaXsqdmGpUSx6dBplqzaQ1FZDdeNSWXJ3M5/91JjLay4dRwbvznNo2v28sDbu0mPC2Nwsmt3c31FiDmAgpJqrDbt89VDJSgWTjFhZpezTxwpraGhydZh5omW/rbpW5ZvKeS2KRlc6QgoLxuaxKtbCvnZDzKJb+eL5XwVNY0cKqnicEm1M/Ctrm/i9dvGA/DyFwV8sv874sLMZCaEcengXs7FcEvXHaSgpJrnbuh8HnR73tx+lLRYC+NduBjwVXWNVkb95mNum5LJ/bMHsOrfJxndJCGE8CvVDVZqG63tjhTHhQczMjWaTw+col9iGHe+/hV94yy8fts4JvZrXVm0PUopftA/gU/uncr+4koG9fa/CpxpsRYarDZOVda1ytThayQoFk6xYWZKXQyKDxZXAbg8/3h0eiyLp/Xjvkv7O5+7c8YlrMk7wf9sLDjnqrnRaqOorMYZ9B45Xc3vrhpKYICJp9YdYMVWe/qwoABFelwYWQnhzhHgx64cyp9+EkC0pfWiQYs5kPf3nORH+cXMGZrkUrtbKiytZktBKff/sL9fF3wICQogKzGcnUX+U2VICCF8xYHiSub8P/t0gOaUY2156IrBRIYE0Sc6lF/MHsCtkzM6X4vSDpNJMSS5nWxHPq45V3FRWY0ExcJ/xFjMnKlxNSiuxKRoc5FBW4alRDEs5dwPdFZiOD8akcyLnxdww9h00uIsvLq1kMfW7KWpRR7d+PBg7ppVT++oUK4bk8aMgYlkxoeTEhPaKoF5R+WKF03JYE3eCR5enc/ErDgiQ4Jcanuzj/edwqTg2tHt5Cb2I6PTY3gn9xi7jpbzwNt5/OGa4WT70YpmIYQwSoBjXu+z/zKKOedXNG2h5XfqHdOzPN4uX5UeGwbYg2Jfv8sqQbFw+t1VQ11OkzYuMw5zoOmCr3qb/ceMS/ggv5hj5TWkxVkYkhzJ7VMzyYwPJzMhjMyEcKJCvw9e7VMhLuxqOSjAxJPXDOOq5zbx5AcHePzqYV16/a2TM5g+MNFjlYq8KTsthr9vKWTHkTK+PnWWJmvrRPFCCCFaa/7dq21w3yLu7qx3dAjzc1JI7UIdBKNIUCycmvMjumJSVjyTslyfF9WerMRwdj50qTPFTXZajEdHLIenRPOvE/qyfMsR7r20P3EuzmUG+9yufgmujYz7uuY+3ny4FJCUbEII4arm4h0PvLObucOSiOjiXceeJijA1Gm+Zl8hv4TCKf94Bc99dogGR5qu9tQ3Wdl3otJtqc7CgwO9uiL15zMv4cslM7sUEN/3Vh5PfnjAg63yrtTYUB6bN4TR6fbgODhIvgqEEMIVLauzhflJpTajaa1dXshvJPklFE55x8p56qODHc4r1lrz2tYiLvuvjaw/WOLF1rlPbJiZxEjXp0CUVNWzetdxrLbuM8VAKcVNE/uSHG3vh+BAyVMshBCuaHlnzZ8XXXvTQ6vzmfX050Y3o1MSFAunWEfGhvZyFdc0NHHfW3n85r19TM6KZ2r/BG82z63e232CpesOurTtqp3HaLJp5uf4/wK7lipqG9n27RlGpEQRHiyjHUII4QqTSTFjYCJD+/hXvmAj9Ym2UF7bSE1Dk9FN6ZAExcIpJsweFLd1i+ObU1XM+8sm/nfXce6Z1Z/lt4y96EV2Rso7Ws6LGwo6nSqitebNHUcZnR7jcqYNf3GwuIo3thXx85mXtJtrUwghRGslVfXtFu4QrS2c2JeDv51zztQTXyRBsXCKdQTFZW1Mn6isa+RsXRMrbh3HXbMu8fmqNJ3JTouhocnG3hMVHW6XW3iGgpJqFozpXqPEAMNTogg0KXILJV+xEEJ0xZ7jFeekDhUdCzUHtEqh6ot8v4XCa2Is544U1zVaWbvnJGAvvvH5A9PcknHCF2Q7Fph1FhAmRASzcGJfLh/Wfi5KfxUSFECTTfP8+sM0WTseMRdCCPG9bx6fy99uHmt0M4Sb+fY4tvCquDAz2x6cSYzFzOGSs9zx2k6+PlXFx/dOpV9CeLdajNUrMoSUmNBOq7qlx4Xx6LwhXmqV96XHWSgsrfH7kX8hhPAmV3P6C/8iQbFwMpkUiREhrN51nF+t2oM50MQrC8d0m9y85xubEUtFTWO7f99aUIpJKcb0jUGp7hk0rrljMgWnz3bb4xNCCCFcJUGxOMdD/5fPq1sLyUmP4dnrR9E7yrfrlF+MpT8ZcU4wWFnXyN3/2MXRsho0cKqyjqTIENbd8wPjGulhUZYgRkl5ZyGEEMK9QbFSqhfwttZ6ijv3K7wnMyGMey/tz+Jp/br97aHzR0fzj1Ww/dsyJmbFEWBSDEiKYEFOqoyiCiGEED2A0to9qyeVUjHAG0Ci1jq7o21zcnL0jh073PK+QlyMRcu30y8xnCVzBwH20eJIKdkphBBCdBtKqVytdU5n27lzKNAKLAAq3bhPITyqpsHKR/nFrNp5DK21BMRCCCFED+W2oFhrXam1bjfpq1LqZ0qpHUqpHSUl/lkeWHQ/o9NjOFJaw5JVezhZUWd0c4QQQghhEK9NGtVav6S1ztFa5yQk+G95YNG9jMuIA+CXcweSHN19FxUKIYQQomOSfUL0aJOy4lj78ykM6h1hdFOEEEIIYSAJikWPppRicHKk0c0QQgghhMHcPn1Caz3N3fsUQgghhBDCk7p3IlohhBBCCCFcIEGxEEIIIYTo8SQoFkIIIYQQPZ4ExUIIIYQQosdzW5nnLr2pUiVAoZfeLh447aX3Eh2Tc+F50se+Q86FsaT/fYecC+NI39ula607LZJhSFDsTUqpHa7UuxaeJ+fC86SPfYecC2NJ//sOORfGkb7vGpk+IYQQQgghejwJioUQQgghRI/XE4Lil4xugHCSc+F50se+Q86FsaT/fYecC+NI33dBt59TLIQQQgghRGd6wkixEEIIIYQQHZKgWIgeTinVWyk1SykVYXRbhBBCCKP4dFCslIpSSn2glFqnlPpfpZRZKfWyUmqLUurXLbbrpZTa2OLf2UqpT5RSm5RS93Ww/yCl1LuO7W5p8fwgpdRqzx2Z/zHiXCil0pRS65VSnyqlXlJKKc8epbEM6uP+wJvAJOBzpZTZg4foN4z67nH8bahS6mPPHJl/MOiz0EcpdczxnbNeKdVpTtOewODPwrtKqZGeOTLfZ9Dn4LEWn4EDSqklnj1K3+LTQTFwA/C01vqHQDFwHRCgtZ4AZCqlLlFKxQDLgbAWr3sWuBmYDFyjlMpoZ///AeRqrScB1yqlIpRS/YCngCjPHJLf8vq5AG4HFmutZwCpwDBPHJgPMaKPhwM3a60fAwqA9l7b0xhxLnBc+D0NBHnioPyIEf0/Dnhcaz3N8SjxzKH5HaM+CzcAh7XWuzxyVP7B632vtX6k+TMA5AN/98iR+SifDoq11s9rrZtHTBKAG4G3HP9eh/2EW4EFQGWLl8ZqrY9q+yrCUiCynbeY1mJ/G4AcoAq4xl3H0F0YcS601g9qrfc7noujm1flMaiP3wYKlVKXAzHAITcdjl8z6LsH7D9kn7njGPyZQf0/HliklNqplPq9u47F3xlxLpRSscBS4IxSarq7jsXfGPg9hFJqDHBMa33cDYfiNwKNboArlFITsP9gHwGaT1AZkK21rnRs0/Ilm5RSdzq26QvsVvbpEC1Hf1/HfmXVcn+9tNaftbE/4eDNc9HiPRcAe7XWJ9x8OD7JgD4OB+ZjL70u6Wha8Oa5UErFYf/Rm+149Hhe/ix8APwWqAE+UUoN11rvdv9R+Scvn4t7gJXAi8ATjhHMNe4/Kv9gxO8ucBfwiDuPwx/4fFDsuGJ8Fvvo7b1AqONP4bQ/0n07MB34DfCk42rpyjb2fbljfxWO/Z11a+O7GSPOhVIqE7gfmOW2A/FhRvSx1rocuEkp9SowBvjSXcfjzww4F38AlmitG+Wi3JD+36y1rnf8/SvgEkCCYgw5F6OA+7XWxUqpt4BLgR4ZFBv0uxsNJGqtD7vvSPyDT0+fUPZFPyux/1AUArnYbxcAjMB+1dSK1toKHHT887UO3sKl/QljzoVjrtQbwC1a64qLOgA/YFAfv6CU+oHjuWig/IIPoBsx6LtnKvCkUmo9MFIp9bsLPwL/ZlD/f6TsmVgswA+xz6fs8Qw6F4eATMdzOdjvYvU4BsZAVwJrL7Tdfk1r7bMPYDFwBljveNwE5GFfiLIfiGqx7frzXrscmNLJ/tOBvcCfge3YJ7C3ub+e/jDiXABPAidbvOdUo/uhG/ZxBvAFsBF4yOg+8JWHkd89be2zpz0M+ixMBw5gHx2+0+g+8JWHQeciGXtQtgn4GIgwuh96St87nn8d+9QMw/vA2w+/q2jnGD28FNigtS52w/6SsV8pfaR7wGikO8m58DzpY98h58JY0v++Q86FcaTvPcvvgmIhhBBCCCHczafnFAshhBBCCOENEhQLIYQQQogeT4JiIYQQQgjR40lQLIQQQgghejwJioUQQgghRI/3/wGF2m555zpgEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigma_plot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [dtt.dat, \"二\", \"三\"]\n",
    "y = [1, 4, 9]\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.plot(x, y)\n",
    "minor_locator = FixedLocator([1, 2])\n",
    "ax.xaxis.set_major_locator(minor_locator)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(1999, 4, 1)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtt.date(1999,4,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class DayLocator in module matplotlib.dates:\n",
      "\n",
      "class DayLocator(RRuleLocator)\n",
      " |  Make ticks on occurrences of each day of the month.  For example,\n",
      " |  1, 15, 30.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      DayLocator\n",
      " |      RRuleLocator\n",
      " |      DateLocator\n",
      " |      matplotlib.ticker.Locator\n",
      " |      matplotlib.ticker.TickHelper\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, bymonthday=None, interval=1, tz=None)\n",
      " |      Mark every day in *bymonthday*; *bymonthday* can be an int or\n",
      " |      sequence.\n",
      " |      \n",
      " |      Default is to tick every day of the month: ``bymonthday=range(1,32)``\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from RRuleLocator:\n",
      " |  \n",
      " |  __call__(self)\n",
      " |      Return the locations of the ticks\n",
      " |  \n",
      " |  autoscale(self)\n",
      " |      Set the view limits to include the data range.\n",
      " |  \n",
      " |  tick_values(self, vmin, vmax)\n",
      " |      Return the values of the located ticks given **vmin** and **vmax**.\n",
      " |      \n",
      " |      .. note::\n",
      " |          To get tick locations with the vmin and vmax values defined\n",
      " |          automatically for the associated :attr:`axis` simply call\n",
      " |          the Locator instance::\n",
      " |      \n",
      " |              >>> print(type(loc))\n",
      " |              <type 'Locator'>\n",
      " |              >>> print(loc())\n",
      " |              [1, 2, 3, 4]\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods inherited from RRuleLocator:\n",
      " |  \n",
      " |  get_unit_generic(freq)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from DateLocator:\n",
      " |  \n",
      " |  datalim_to_dt(self)\n",
      " |      Convert axis data interval to datetime objects.\n",
      " |  \n",
      " |  nonsingular(self, vmin, vmax)\n",
      " |      Given the proposed upper and lower extent, adjust the range\n",
      " |      if it is too close to being singular (i.e. a range of ~0).\n",
      " |  \n",
      " |  set_tzinfo(self, tz)\n",
      " |      Set time zone info.\n",
      " |  \n",
      " |  viewlim_to_dt(self)\n",
      " |      Converts the view interval to datetime objects.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from DateLocator:\n",
      " |  \n",
      " |  hms0d = {'byhour': 0, 'byminute': 0, 'bysecond': 0}\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from matplotlib.ticker.Locator:\n",
      " |  \n",
      " |  pan(self, numsteps)\n",
      " |      Pan numticks (can be positive or negative)\n",
      " |  \n",
      " |  raise_if_exceeds(self, locs)\n",
      " |      raise a RuntimeError if Locator attempts to create more than\n",
      " |      MAXTICKS locs\n",
      " |  \n",
      " |  refresh(self)\n",
      " |      refresh internal information based on current lim\n",
      " |  \n",
      " |  set_params(self, **kwargs)\n",
      " |      Do nothing, and rase a warning. Any locator class not supporting the\n",
      " |      set_params() function will call this.\n",
      " |  \n",
      " |  view_limits(self, vmin, vmax)\n",
      " |      select a scale for the range from vmin to vmax\n",
      " |      \n",
      " |      Normally this method is overridden by subclasses to\n",
      " |      change locator behaviour.\n",
      " |  \n",
      " |  zoom(self, direction)\n",
      " |      Zoom in/out on axis; if direction is >0 zoom in, else zoom out\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from matplotlib.ticker.Locator:\n",
      " |  \n",
      " |  MAXTICKS = 1000\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from matplotlib.ticker.TickHelper:\n",
      " |  \n",
      " |  create_dummy_axis(self, **kwargs)\n",
      " |  \n",
      " |  set_axis(self, axis)\n",
      " |  \n",
      " |  set_bounds(self, vmin, vmax)\n",
      " |  \n",
      " |  set_data_interval(self, vmin, vmax)\n",
      " |  \n",
      " |  set_view_interval(self, vmin, vmax)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from matplotlib.ticker.TickHelper:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from matplotlib.ticker.TickHelper:\n",
      " |  \n",
      " |  axis = None\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(DayLocator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.dates.DayLocator at 0x1fe8fa83be0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DayLocator([1, 15, 30])"
   ]
  }
 ],
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